2025-03-14T04:12:09.5227295Z Current runner version: '2.322.0'
2025-03-14T04:12:09.5233136Z Runner name: 'i-047a1559c2de50868'
2025-03-14T04:12:09.5233788Z Runner group name: 'Default'
2025-03-14T04:12:09.5234668Z Machine name: 'ip-10-0-71-89'
2025-03-14T04:12:09.5237960Z ##[group]GITHUB_TOKEN Permissions
2025-03-14T04:12:09.5240489Z Actions: read
2025-03-14T04:12:09.5241118Z Attestations: read
2025-03-14T04:12:09.5241606Z Checks: read
2025-03-14T04:12:09.5242071Z Contents: read
2025-03-14T04:12:09.5242563Z Deployments: read
2025-03-14T04:12:09.5243039Z Discussions: read
2025-03-14T04:12:09.5243488Z Issues: read
2025-03-14T04:12:09.5243970Z Metadata: read
2025-03-14T04:12:09.5244432Z Packages: read
2025-03-14T04:12:09.5244896Z Pages: read
2025-03-14T04:12:09.5245385Z PullRequests: read
2025-03-14T04:12:09.5245966Z RepositoryProjects: read
2025-03-14T04:12:09.5246537Z SecurityEvents: read
2025-03-14T04:12:09.5246997Z Statuses: read
2025-03-14T04:12:09.5247462Z ##[endgroup]
2025-03-14T04:12:09.5250614Z Secret source: Actions
2025-03-14T04:12:09.5251519Z Prepare workflow directory
2025-03-14T04:12:09.8816766Z Prepare all required actions
2025-03-14T04:12:09.8855297Z Getting action download info
2025-03-14T04:12:10.0502311Z Download action repository 'pytorch/test-infra@main' (SHA:de00dac6adc071cb2f9861380a0ed3947b93e5cc)
2025-03-14T04:12:10.9214735Z Download action repository 'pytorch/pytorch@main' (SHA:e5679009988279a3059f398ed2077a0477099ba6)
2025-03-14T04:12:24.2523613Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2025-03-14T04:12:24.4988140Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-03-14T04:12:24.8227762Z Getting action download info
2025-03-14T04:12:24.9225867Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683)
2025-03-14T04:12:25.1493778Z Getting action download info
2025-03-14T04:12:25.2383363Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-03-14T04:12:25.4090244Z Getting action download info
2025-03-14T04:12:25.4922135Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2025-03-14T04:12:25.6352704Z Getting action download info
2025-03-14T04:12:25.7594725Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (aed0b7a742a2d7b7901790622829cbd2135049a4)
2025-03-14T04:12:25.7596470Z ##[group] Inputs
2025-03-14T04:12:25.7596741Z   build-environment: linux-jammy-py3.9-gcc11-build
2025-03-14T04:12:25.7598362Z   test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}
2025-03-14T04:12:25.7600176Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:25.7600682Z   sync-tag: 
2025-03-14T04:12:25.7601354Z   timeout-minutes: 240
2025-03-14T04:12:25.7601552Z   use-gha: 
2025-03-14T04:12:25.7601734Z   dashboard-tag: 
2025-03-14T04:12:25.7601949Z   s3-bucket: gha-artifacts
2025-03-14T04:12:25.7602171Z   aws-role-to-assume: 
2025-03-14T04:12:25.7602879Z   disable-monitor: false
2025-03-14T04:12:25.7603200Z ##[endgroup]
2025-03-14T04:12:25.7603553Z Complete job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:12:25.8001397Z A job started hook has been configured by the self-hosted runner administrator
2025-03-14T04:12:25.8104819Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2025-03-14T04:12:25.8111872Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:25.8112318Z ##[endgroup]
2025-03-14T04:12:27.0877268Z Runner Type: linux.8xlarge.amx
2025-03-14T04:12:27.0877758Z Instance Type: m7i-flex.8xlarge
2025-03-14T04:12:27.0877973Z AMI Name: unknown
2025-03-14T04:12:27.0902945Z AMI ID: ami-08b5b3a93ed654d19
2025-03-14T04:12:31.4359108Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2025-03-14T04:12:31.4359444Z with:
2025-03-14T04:12:31.4360102Z   github-secret: ***
2025-03-14T04:12:31.4360557Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2025-03-14T04:12:31.4361043Z   activate-with-label: false
2025-03-14T04:12:31.4361248Z   label: with-ssh
2025-03-14T04:12:31.4361437Z   remove-existing-keys: true
2025-03-14T04:12:31.4361629Z   fail-silently: true
2025-03-14T04:12:31.4361813Z env:
2025-03-14T04:12:31.4361983Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:31.4362189Z ##[endgroup]
2025-03-14T04:12:31.5337838Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2025-03-14T04:12:31.5338470Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys
2025-03-14T04:12:31.5483202Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-03-14T04:12:31.5483519Z with:
2025-03-14T04:12:31.5483702Z   no-sudo: true
2025-03-14T04:12:31.5483896Z   submodules: recursive
2025-03-14T04:12:31.5484094Z   fetch-depth: 0
2025-03-14T04:12:31.5484262Z env:
2025-03-14T04:12:31.5484435Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:31.5484647Z ##[endgroup]
2025-03-14T04:12:31.5555479Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:31.5556087Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:31.5563842Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:31.5564103Z env:
2025-03-14T04:12:31.5564319Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:31.5564572Z ##[endgroup]
2025-03-14T04:12:31.5660898Z ##[group]Run # Use all available CPUs for fetching
2025-03-14T04:12:31.5661245Z # Use all available CPUs for fetching
2025-03-14T04:12:31.5661486Z cd "${GITHUB_WORKSPACE}"
2025-03-14T04:12:31.5661732Z git config --global fetch.parallel 0
2025-03-14T04:12:31.5661993Z git config --global submodule.fetchJobs 0
2025-03-14T04:12:31.5662224Z 
2025-03-14T04:12:31.5662479Z # Clean workspace. The default checkout action should also do this, but
2025-03-14T04:12:31.5662783Z # do it here as well just in case
2025-03-14T04:12:31.5662995Z if [[ -d .git ]]; then
2025-03-14T04:12:31.5663204Z   if [ -z "${NO_SUDO}" ]; then
2025-03-14T04:12:31.5663422Z     sudo git clean -ffdx
2025-03-14T04:12:31.5663619Z   else
2025-03-14T04:12:31.5663800Z     git clean -ffdx
2025-03-14T04:12:31.5664123Z   fi
2025-03-14T04:12:31.5664299Z fi
2025-03-14T04:12:31.5669263Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:31.5669578Z env:
2025-03-14T04:12:31.5669853Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:31.5670067Z   NO_SUDO: true
2025-03-14T04:12:31.5670243Z ##[endgroup]
2025-03-14T04:12:32.0118757Z Removing .additional_ci_files/
2025-03-14T04:12:32.0119562Z Removing benchmarks/dynamo/__pycache__/
2025-03-14T04:12:32.0119812Z Removing build/
2025-03-14T04:12:32.0120650Z Removing dist/
2025-03-14T04:12:32.0121002Z Removing logs-test-dynamic_cpu_inductor_torchbench-2-2-linux.8xlarge.amx_38753240423.zip
2025-03-14T04:12:32.0121342Z Removing stargan/
2025-03-14T04:12:32.0121665Z Removing test-jsons-test-dynamic_cpu_inductor_torchbench-2-2-linux.8xlarge.amx_38753240423.zip
2025-03-14T04:12:32.0122144Z Removing test-reports-test-dynamic_cpu_inductor_torchbench-2-2-linux.8xlarge.amx_38753240423.zip
2025-03-14T04:12:32.0122506Z Removing test/test-reports/
2025-03-14T04:12:32.0122720Z Removing tools/__pycache__/
2025-03-14T04:12:32.0123008Z Removing tools/stats/__pycache__/
2025-03-14T04:12:32.0123380Z Removing tools/stats/upload_utilization_stats/__pycache__/
2025-03-14T04:12:32.0123650Z Removing torchbench/
2025-03-14T04:12:32.0123843Z Removing usage_log.txt
2025-03-14T04:12:32.0200252Z ##[group]Run actions/checkout@v4
2025-03-14T04:12:32.0200490Z with:
2025-03-14T04:12:32.0200688Z   ref: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:32.0200938Z   fetch-depth: 0
2025-03-14T04:12:32.0201123Z   submodules: recursive
2025-03-14T04:12:32.0201315Z   show-progress: false
2025-03-14T04:12:32.0201510Z   repository: pytorch/pytorch
2025-03-14T04:12:32.0201781Z   token: ***
2025-03-14T04:12:32.0201953Z   ssh-strict: true
2025-03-14T04:12:32.0202126Z   ssh-user: git
2025-03-14T04:12:32.0202310Z   persist-credentials: true
2025-03-14T04:12:32.0202506Z   clean: true
2025-03-14T04:12:32.0202688Z   sparse-checkout-cone-mode: true
2025-03-14T04:12:32.0202888Z   fetch-tags: false
2025-03-14T04:12:32.0203061Z   lfs: false
2025-03-14T04:12:32.0203239Z   set-safe-directory: true
2025-03-14T04:12:32.0203609Z env:
2025-03-14T04:12:32.0203787Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:32.0203976Z ##[endgroup]
2025-03-14T04:12:32.1149129Z Syncing repository: pytorch/pytorch
2025-03-14T04:12:32.1150281Z ##[group]Getting Git version info
2025-03-14T04:12:32.1150622Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-14T04:12:32.1151123Z [command]/usr/bin/git version
2025-03-14T04:12:32.1151355Z git version 2.47.1
2025-03-14T04:12:32.1158038Z ##[endgroup]
2025-03-14T04:12:32.1168854Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/08a9ab18-1fea-43a3-b933-c459782c105f/.gitconfig'
2025-03-14T04:12:32.1190911Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/08a9ab18-1fea-43a3-b933-c459782c105f' before making global git config changes
2025-03-14T04:12:32.1191537Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T04:12:32.1199430Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:12:32.1246142Z [command]/usr/bin/git config --local --get remote.origin.url
2025-03-14T04:12:32.1267290Z https://github.com/pytorch/pytorch
2025-03-14T04:12:32.1284534Z ##[group]Removing previously created refs, to avoid conflicts
2025-03-14T04:12:32.1290001Z [command]/usr/bin/git rev-parse --symbolic-full-name --verify --quiet HEAD
2025-03-14T04:12:32.1310239Z HEAD
2025-03-14T04:12:32.1368110Z ##[endgroup]
2025-03-14T04:12:32.1372181Z [command]/usr/bin/git submodule status
2025-03-14T04:12:32.1751044Z  7e1e1fe3858c63c251c637ae41a20de425dde96f android/libs/fbjni (v0.1.0-12-g7e1e1fe)
2025-03-14T04:12:32.1818147Z  4dfe081cf6bcd15db339cf2680b9281b8451eeb3 third_party/FP16 (4dfe081)
2025-03-14T04:12:32.1882515Z  b408327ac2a15ec3e43352421954f5b1967701d1 third_party/FXdiv (b408327)
2025-03-14T04:12:32.1969518Z  c07e3a0400713d546e0dea2d5466dd22ea389c73 third_party/NNPACK (c07e3a0)
2025-03-14T04:12:32.1988657Z  e170594ac7cf1dac584da473d4ca9301087090c1 third_party/NVTX (v3.1.0)
2025-03-14T04:12:32.2054050Z  a6bfc237255a6bac1513f7c1ebde6d8aed6b5191 third_party/VulkanMemoryAllocator (v2.1.0-705-ga6bfc23)
2025-03-14T04:12:32.2623460Z  51a0103656eff6fc9bfd39a4597923c4b542c883 third_party/XNNPACK (remotes/origin/ds/ndk-1243-g51a010365)
2025-03-14T04:12:32.2650629Z  0d98dba29d66e93259db7daa53a9327df767a415 third_party/benchmark (v1.6.1)
2025-03-14T04:12:32.2679474Z  8086bbe3a78d931eb96fe12fdc014082e18d18d3 third_party/composable_kernel (mock-tag-test-6-g8086bbe3a)
2025-03-14T04:12:32.2801486Z  3b6597bba913d51161383657829b7e644e59c006 third_party/cpp-httplib (v0.15.3-20-g3b6597b)
2025-03-14T04:12:32.2906792Z  1e83a2fdd3102f65c6f1fb602c1b320486218a99 third_party/cpuinfo (1e83a2f)
2025-03-14T04:12:32.2941277Z  91b7532f3386768bba4f444ee7672b497f34da8a third_party/cudnn_frontend (v0.5-44-g91b7532)
2025-03-14T04:12:32.3025871Z  afa1772203677c5118fcd82537a9c8fefbcc7008 third_party/cutlass (v3.8.0)
2025-03-14T04:12:32.3594740Z  3147391d946bb4b6c68edd901f2add6ac1f31f8c third_party/eigen (3.4.0)
2025-03-14T04:12:32.3912975Z  dbc3157bf256f1339b3fa1fef2be89ac4078be0e third_party/fbgemm (v0.4.1-446-gdbc3157b)
2025-03-14T04:12:32.3994906Z  979702c87a8713a8e0a5e9fee122b90d2ef13be5 third_party/flash-attention (v2.7.4)
2025-03-14T04:12:32.4017947Z  01834de25e4bf3975a9a00e816292b1ad0fe184b third_party/flatbuffers (v23.3.3)
2025-03-14T04:12:32.4471725Z  123913715afeb8a437e6388b4473fcc4753e1c9a third_party/fmt (11.1.4)
2025-03-14T04:12:32.4569572Z  3fb5c176c17c765a3492cd2f0321b0dab712f350 third_party/gemmlowp/gemmlowp (remotes/origin/revert-87-master-135-g3fb5c17)
2025-03-14T04:12:32.4673613Z  5354032ea08eadd7fc4456477f7f7c6308818509 third_party/gloo (5354032)
2025-03-14T04:12:32.4887732Z  b514bdc898e2951020cbdca1304b75f5950d1f59 third_party/googletest (release-1.8.0-3484-gb514bdc8)
2025-03-14T04:12:32.4963946Z  719d8e6cd7f7a0e01b155657526d693acf97c2b3 third_party/ideep (pytorch-rls-v3.7.1)
2025-03-14T04:12:32.5019319Z  5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42 third_party/ittapi (v3.23.0-14-g5b8a7d7)
2025-03-14T04:12:32.5250021Z  2859721fd9e73d3ca1c56f827dbc64e6d68f78a2 third_party/kineto (remotes/origin/sraikund/test-53-g2859721)
2025-03-14T04:12:32.5271747Z  ef685a13cfbe8d418aa2ed34350e21e4938358b6 third_party/kleidiai (v1.3.0)
2025-03-14T04:12:32.5292839Z  b66e3214d8a104669c2ec05ae91ebc26a8f5ab78 third_party/mimalloc (v1.8.2)
2025-03-14T04:12:32.5717278Z  87cda1d6646592ac5866dc703c8e1839046a6806 third_party/nlohmann (v3.10.1-113-g87cda1d6)
2025-03-14T04:12:32.5985975Z  b8baa8446686496da4cc8fda09f2b6fe65c2a02c third_party/onnx (v1.17.0)
2025-03-14T04:12:32.6007508Z  a799f4aed9c94b765dcdaabaeab7d5e7e2310878 third_party/opentelemetry-cpp (v1.14.2)
2025-03-14T04:12:32.6026891Z  9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe third_party/pocketfft (release_for_eigen-11-g9d3ab05)
2025-03-14T04:12:32.6402912Z  d1eca4e4b421cd2997495c4b4e65cea6be4e9b8a third_party/protobuf (v3.7.0-rc.2-1279-gd1eca4e4b)
2025-03-14T04:12:32.6473874Z  072586a71b55b7f8c584153d223e95687148a900 third_party/psimd (heads/master)
2025-03-14T04:12:32.6523928Z  4fe0e1e183925bf8cfa6aae24237e724a96479b8 third_party/pthreadpool (0.1-144-g4fe0e1e)
2025-03-14T04:12:32.6571266Z  a2e59f0e7065404b44dfe92a28aca47ba1378dc4 third_party/pybind11 (v2.11.0-182-ga2e59f0e)
2025-03-14T04:12:32.6640708Z  f45429b087dd7d5bc78bb40dc7cf06425c252d67 third_party/python-peachpy (remotes/origin/pre-generated)
2025-03-14T04:12:32.6739059Z  56e1f79cb140fb9326d612d0be06b5250565cade third_party/sleef (3.7-33-g56e1f79)
2025-03-14T04:12:32.6799301Z  52791a2fd214b2a9dc5759d36725909c1daa7f2e third_party/tensorpipe (remotes/origin/master)
2025-03-14T04:12:32.6813952Z ##[group]Cleaning the repository
2025-03-14T04:12:32.6820032Z [command]/usr/bin/git clean -ffdx
2025-03-14T04:12:32.6980601Z [command]/usr/bin/git reset --hard HEAD
2025-03-14T04:12:33.6643719Z HEAD is now at b4745db9048 [MPS] Add support for `i0e` in eager. (#149174)
2025-03-14T04:12:33.6690266Z ##[endgroup]
2025-03-14T04:12:33.6690826Z ##[group]Disabling automatic garbage collection
2025-03-14T04:12:33.6693799Z [command]/usr/bin/git config --local gc.auto 0
2025-03-14T04:12:33.6729978Z ##[endgroup]
2025-03-14T04:12:33.6734888Z ##[group]Setting up auth
2025-03-14T04:12:33.6739757Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T04:12:33.6760706Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
2025-03-14T04:12:33.7058774Z Entering 'android/libs/fbjni'
2025-03-14T04:12:33.7109556Z Entering 'third_party/FP16'
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2025-03-14T04:12:34.0891822Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
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2025-03-14T04:12:34.5077512Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-14T04:12:34.5138156Z ##[endgroup]
2025-03-14T04:12:34.5138870Z ##[group]Fetching the repository
2025-03-14T04:12:34.5139384Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-03-14T04:12:34.7530455Z From https://github.com/pytorch/pytorch
2025-03-14T04:12:34.7531073Z  - [deleted]                 (none)                  -> ciflow/inductor/149162
2025-03-14T04:12:34.7791809Z  - [deleted]                 (none)                  -> ciflow/trunk/149166
2025-03-14T04:12:36.1031283Z    b4745db9048..e5679009988  main                    -> origin/main
2025-03-14T04:12:36.1037844Z  + e2f3d057bea...c4b8be3d09f update_submodule_FBGEMM -> origin/update_submodule_FBGEMM  (forced update)
2025-03-14T04:12:36.1038388Z    4098a229a04..49570cb4024  viable/strict           -> origin/viable/strict
2025-03-14T04:12:36.1038844Z    1579a02c823..3ffd7552df1  wdvr/iss145259_alt      -> origin/wdvr/iss145259_alt
2025-03-14T04:12:36.1050152Z  * [new tag]                 ciflow/trunk/146289     -> ciflow/trunk/146289
2025-03-14T04:12:36.1050627Z  * [new tag]                 ciflow/trunk/149064     -> ciflow/trunk/149064
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2025-03-14T04:12:36.5248698Z Previous HEAD position was b4745db9048 [MPS] Add support for `i0e` in eager. (#149174)
2025-03-14T04:12:36.5255197Z HEAD is now at aed0b7a742a [c10d] Add param recording for uniqueID broadcasting and allgather (#149166)
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2025-03-14T04:12:36.6783689Z Synchronizing submodule url for 'third_party/kineto/libkineto/third_party/fmt'
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2025-03-14T04:12:36.7520497Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-03-14T04:12:36.8024598Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-03-14T04:12:36.8133186Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-03-14T04:12:36.8217743Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-03-14T04:12:36.8443554Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-03-14T04:12:36.8757284Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2025-03-14T04:12:36.9104703Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2025-03-14T04:12:37.5335474Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-03-14T04:12:37.5545900Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2025-03-14T04:12:37.7668397Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3'
2025-03-14T04:12:37.8045718Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2025-03-14T04:12:37.8915286Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2025-03-14T04:12:37.9229373Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a'
2025-03-14T04:12:38.4671638Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008'
2025-03-14T04:12:38.6951490Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2025-03-14T04:12:38.7519403Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2025-03-14T04:12:38.7907910Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2025-03-14T04:12:38.8739971Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2025-03-14T04:12:39.2375608Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2025-03-14T04:12:39.2779656Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2025-03-14T04:12:39.2889574Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2025-03-14T04:12:39.3527410Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-03-14T04:12:39.5625363Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-03-14T04:12:40.1129413Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-03-14T04:12:40.2328273Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2025-03-14T04:12:40.2617320Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a'
2025-03-14T04:12:40.2983763Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-03-14T04:12:40.3222078Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2025-03-14T04:12:40.3616389Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59'
2025-03-14T04:12:40.3734995Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3'
2025-03-14T04:12:40.8570146Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d'
2025-03-14T04:12:40.8720650Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2025-03-14T04:12:40.9576747Z Submodule path 'third_party/kineto': checked out '2859721fd9e73d3ca1c56f827dbc64e6d68f78a2'
2025-03-14T04:12:41.0338314Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-03-14T04:12:41.1980332Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-03-14T04:12:41.2152256Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-03-14T04:12:41.2466872Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-03-14T04:12:41.2602643Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-03-14T04:12:41.2681270Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-03-14T04:12:41.2845517Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-03-14T04:12:41.3200491Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-03-14T04:12:41.4096245Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-03-14T04:12:41.4251616Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-03-14T04:12:41.4589254Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-03-14T04:12:41.4998507Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-03-14T04:12:41.5322325Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6'
2025-03-14T04:12:41.5672145Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2025-03-14T04:12:41.6601673Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2025-03-14T04:12:41.9564387Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2025-03-14T04:12:41.9947859Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2025-03-14T04:12:42.0570949Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-03-14T04:12:42.0755423Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-03-14T04:12:42.1117279Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2025-03-14T04:12:42.1233140Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2025-03-14T04:12:42.2194301Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2025-03-14T04:12:42.2325390Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2025-03-14T04:12:42.2457834Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2025-03-14T04:12:42.2592952Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2025-03-14T04:12:42.4824441Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2025-03-14T04:12:42.5224264Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2025-03-14T04:12:42.9175235Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2025-03-14T04:12:42.9303408Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe'
2025-03-14T04:12:43.1604057Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2025-03-14T04:12:43.1741812Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2025-03-14T04:12:43.2178610Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2025-03-14T04:12:43.2266735Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2025-03-14T04:12:43.2384300Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2025-03-14T04:12:43.2699979Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-03-14T04:12:43.2980600Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2025-03-14T04:12:43.3375845Z Submodule path 'third_party/sleef': checked out '56e1f79cb140fb9326d612d0be06b5250565cade'
2025-03-14T04:12:43.3611306Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e'
2025-03-14T04:12:43.3996074Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2025-03-14T04:12:43.4140171Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2025-03-14T04:12:43.4696201Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242'
2025-03-14T04:12:43.4994776Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2025-03-14T04:12:43.5075813Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2025-03-14T04:12:43.5121014Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2025-03-14T04:12:43.5450738Z Entering 'android/libs/fbjni'
2025-03-14T04:12:43.5486273Z Entering 'third_party/FP16'
2025-03-14T04:12:43.5528768Z Entering 'third_party/FXdiv'
2025-03-14T04:12:43.5612163Z Entering 'third_party/NNPACK'
2025-03-14T04:12:43.5645407Z Entering 'third_party/NVTX'
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2025-03-14T04:12:43.6063469Z Entering 'third_party/eigen'
2025-03-14T04:12:43.6100454Z Entering 'third_party/fbgemm'
2025-03-14T04:12:43.6155579Z Entering 'third_party/fbgemm/third_party/asmjit'
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2025-03-14T04:12:43.6209351Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:43.6371413Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:43.6428125Z Entering 'third_party/fbgemm/third_party/hipify_torch'
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2025-03-14T04:12:43.6644718Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:43.6680811Z Entering 'third_party/gloo'
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2025-03-14T04:12:43.6772480Z Entering 'third_party/ideep'
2025-03-14T04:12:43.6789521Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:43.6834018Z Entering 'third_party/ittapi'
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2025-03-14T04:12:43.6909592Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:43.6942315Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:43.6982326Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
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2025-03-14T04:12:43.7146022Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:43.7178051Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:43.7219576Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:43.7256434Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:43.7291892Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:43.7348126Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:43.7421326Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:43.7455782Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:43.7495213Z Entering 'third_party/kleidiai'
2025-03-14T04:12:43.7529923Z Entering 'third_party/mimalloc'
2025-03-14T04:12:43.7568211Z Entering 'third_party/nlohmann'
2025-03-14T04:12:43.7604078Z Entering 'third_party/onnx'
2025-03-14T04:12:43.7651803Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:43.7709455Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:43.7725322Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:43.7759993Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:43.7795429Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:43.7835687Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:43.7869714Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:43.7906231Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:43.7947973Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:43.7977195Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:43.8082537Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:43.8124267Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:43.8179951Z Entering 'third_party/pocketfft'
2025-03-14T04:12:43.8216790Z Entering 'third_party/protobuf'
2025-03-14T04:12:43.8255212Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:43.8290163Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:43.8330671Z Entering 'third_party/psimd'
2025-03-14T04:12:43.8366745Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:43.8404472Z Entering 'third_party/pybind11'
2025-03-14T04:12:43.8488854Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:43.8490792Z Entering 'third_party/sleef'
2025-03-14T04:12:43.8548875Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:43.8549430Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:43.8632162Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:43.8653344Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:43.8672196Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:43.8704404Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:43.8757675Z ##[endgroup]
2025-03-14T04:12:43.8758161Z ##[group]Persisting credentials for submodules
2025-03-14T04:12:43.8764962Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
2025-03-14T04:12:43.9059992Z Entering 'android/libs/fbjni'
2025-03-14T04:12:43.9087694Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9093118Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9125375Z Entering 'third_party/FP16'
2025-03-14T04:12:43.9173712Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9174486Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9191064Z Entering 'third_party/FXdiv'
2025-03-14T04:12:43.9222571Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9223074Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9256519Z Entering 'third_party/NNPACK'
2025-03-14T04:12:43.9285180Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9286999Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9315127Z Entering 'third_party/NVTX'
2025-03-14T04:12:43.9343306Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9343787Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9381020Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:43.9414724Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9415365Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9447283Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:43.9476160Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9476588Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9527949Z Entering 'third_party/benchmark'
2025-03-14T04:12:43.9559697Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9561563Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9597189Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:43.9631260Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9636360Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9665941Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:43.9701798Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9702182Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9736719Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:43.9766841Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9767411Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9800338Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:43.9829717Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9830134Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9865718Z Entering 'third_party/cutlass'
2025-03-14T04:12:43.9895628Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9898052Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9953397Z Entering 'third_party/eigen'
2025-03-14T04:12:43.9979824Z url.https://github.com/.insteadof
2025-03-14T04:12:43.9980442Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0017497Z Entering 'third_party/fbgemm'
2025-03-14T04:12:44.0045952Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0046461Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0078790Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:44.0111485Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0116405Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0145922Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:44.0179184Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0179568Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0211602Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:44.0242569Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0242961Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0279878Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:44.0311220Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0311652Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0363699Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:44.0376928Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0379244Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0419181Z Entering 'third_party/flash-attention'
2025-03-14T04:12:44.0448871Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0449478Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0485826Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:44.0515497Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0516185Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0587656Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:44.0588274Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0588796Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0677609Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:44.0678237Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0678668Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0697484Z Entering 'third_party/fmt'
2025-03-14T04:12:44.0728272Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0732599Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0759618Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:44.0790969Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0793042Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0823757Z Entering 'third_party/gloo'
2025-03-14T04:12:44.0854092Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0859138Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0910033Z Entering 'third_party/googletest'
2025-03-14T04:12:44.0938952Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0939598Z url.https://github.com/.insteadof
2025-03-14T04:12:44.0981189Z Entering 'third_party/ideep'
2025-03-14T04:12:44.1008264Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1008840Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1051135Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:44.1086294Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1088560Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1184614Z Entering 'third_party/ittapi'
2025-03-14T04:12:44.1205700Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1206170Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1237967Z Entering 'third_party/kineto'
2025-03-14T04:12:44.1268459Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1273078Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1303064Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:44.1333474Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1335046Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1419420Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:44.1430289Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1430660Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1461322Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:44.1492664Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1497483Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1523655Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:44.1554267Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1556207Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1585297Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:44.1618712Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1619324Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1652991Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:44.1678209Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1678783Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1715245Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:44.1746318Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1746877Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1780988Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:44.1815575Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1817566Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1843768Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:44.1872281Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1872752Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1926547Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:44.1960908Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1961402Z url.https://github.com/.insteadof
2025-03-14T04:12:44.1970308Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:44.2001422Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2032339Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2032864Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:44.2061578Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2062104Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2100721Z Entering 'third_party/kleidiai'
2025-03-14T04:12:44.2130347Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2130966Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2162083Z Entering 'third_party/mimalloc'
2025-03-14T04:12:44.2199139Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2204075Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2225121Z Entering 'third_party/nlohmann'
2025-03-14T04:12:44.2255413Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2255928Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2352804Z Entering 'third_party/onnx'
2025-03-14T04:12:44.2484036Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2484964Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2485411Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:44.2485892Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2486265Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2533532Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:44.2562420Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2562958Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2577503Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:44.2618742Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2619383Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2646707Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:44.2675785Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2676296Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2714751Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:44.2746116Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2759244Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2795803Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:44.2807895Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2812584Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2854034Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:44.2875587Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2875969Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2911619Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:44.2941697Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2942288Z url.https://github.com/.insteadof
2025-03-14T04:12:44.2972834Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:44.3005280Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3009439Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3039664Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:44.3067917Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3068297Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3110285Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:44.3147079Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3147709Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3198860Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:44.3218147Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3218712Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3271643Z Entering 'third_party/pocketfft'
2025-03-14T04:12:44.3313622Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3324057Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3344667Z Entering 'third_party/protobuf'
2025-03-14T04:12:44.3375603Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3376032Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3421495Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:44.3465785Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3466648Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3487622Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:44.3519102Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3522986Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3554566Z Entering 'third_party/psimd'
2025-03-14T04:12:44.3581147Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3581749Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3619465Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:44.3660664Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3661606Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3684214Z Entering 'third_party/pybind11'
2025-03-14T04:12:44.3716278Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3721343Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3750019Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:44.3781098Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3782430Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3815580Z Entering 'third_party/sleef'
2025-03-14T04:12:44.3847513Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3852180Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3878149Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:44.3911636Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3914040Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3945859Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:44.3979423Z url.https://github.com/.insteadof
2025-03-14T04:12:44.3979818Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4013366Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:44.4044583Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4049624Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4072729Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:44.4110530Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4114887Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4142438Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:44.4173101Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4173575Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4214791Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:44.4265117Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4296837Z url.https://github.com/.insteadof
2025-03-14T04:12:44.4304491Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2025-03-14T04:12:44.4614331Z Entering 'android/libs/fbjni'
2025-03-14T04:12:44.4665098Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-03-14T04:12:44.4680170Z Entering 'third_party/FP16'
2025-03-14T04:12:44.4726857Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-03-14T04:12:44.4743818Z Entering 'third_party/FXdiv'
2025-03-14T04:12:44.4789506Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-03-14T04:12:44.4806168Z Entering 'third_party/NNPACK'
2025-03-14T04:12:44.4851876Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-03-14T04:12:44.4862857Z Entering 'third_party/NVTX'
2025-03-14T04:12:44.4909065Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
2025-03-14T04:12:44.4935103Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:44.4978479Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
2025-03-14T04:12:44.4997459Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:44.5041427Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
2025-03-14T04:12:44.5067614Z Entering 'third_party/benchmark'
2025-03-14T04:12:44.5113218Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:44.5153621Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:44.5187264Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2025-03-14T04:12:44.5208033Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:44.5252399Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
2025-03-14T04:12:44.5265834Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:44.5312071Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:12:44.5330613Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:44.5382740Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
2025-03-14T04:12:44.5389544Z Entering 'third_party/cutlass'
2025-03-14T04:12:44.5429676Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:12:44.5452565Z Entering 'third_party/eigen'
2025-03-14T04:12:44.5515564Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2025-03-14T04:12:44.5531610Z Entering 'third_party/fbgemm'
2025-03-14T04:12:44.5577248Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2025-03-14T04:12:44.5591785Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:44.5638284Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
2025-03-14T04:12:44.5653246Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:44.5695014Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
2025-03-14T04:12:44.5710312Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:44.5752950Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config	remote.origin.url
2025-03-14T04:12:44.5771896Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:44.5817345Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.5835790Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:44.5874757Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
2025-03-14T04:12:44.5893009Z Entering 'third_party/flash-attention'
2025-03-14T04:12:44.5934598Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config	remote.origin.url
2025-03-14T04:12:44.5949673Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:44.5990646Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
2025-03-14T04:12:44.6012241Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:44.6058424Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
2025-03-14T04:12:44.6080847Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:44.6128519Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-03-14T04:12:44.6143908Z Entering 'third_party/fmt'
2025-03-14T04:12:44.6189353Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:44.6207991Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:44.6256641Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
2025-03-14T04:12:44.6268957Z Entering 'third_party/gloo'
2025-03-14T04:12:44.6316663Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
2025-03-14T04:12:44.6331762Z Entering 'third_party/googletest'
2025-03-14T04:12:44.6377129Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.6398476Z Entering 'third_party/ideep'
2025-03-14T04:12:44.6442109Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/config	remote.origin.url
2025-03-14T04:12:44.6457982Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:44.6502990Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ideep/modules/mkl-dnn/config	remote.origin.url
2025-03-14T04:12:44.6526288Z Entering 'third_party/ittapi'
2025-03-14T04:12:44.6571016Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/ittapi/config	remote.origin.url
2025-03-14T04:12:44.6593412Z Entering 'third_party/kineto'
2025-03-14T04:12:44.6643667Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/config	remote.origin.url
2025-03-14T04:12:44.6652465Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:44.6693285Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/config	remote.origin.url
2025-03-14T04:12:44.6710714Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:44.6753483Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/DCGM/config	remote.origin.url
2025-03-14T04:12:44.6765002Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:44.6809574Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/cpr/config	remote.origin.url
2025-03-14T04:12:44.6828880Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:44.6872167Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:44.6888263Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:44.6933751Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/config	remote.origin.url
2025-03-14T04:12:44.6943450Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:44.6987188Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/gflags/modules/doc/config	remote.origin.url
2025-03-14T04:12:44.7008618Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:44.7095537Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/glog/config	remote.origin.url
2025-03-14T04:12:44.7104922Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:44.7149315Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.7163136Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:44.7209727Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/json/config	remote.origin.url
2025-03-14T04:12:44.7222813Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:44.7267342Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/dynolog/modules/third_party/pfs/config	remote.origin.url
2025-03-14T04:12:44.7287661Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:44.7332317Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/fmt/config	remote.origin.url
2025-03-14T04:12:44.7349774Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:44.7391025Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kineto/modules/libkineto/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.7406964Z Entering 'third_party/kleidiai'
2025-03-14T04:12:44.7446747Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/kleidiai/config	remote.origin.url
2025-03-14T04:12:44.7467057Z Entering 'third_party/mimalloc'
2025-03-14T04:12:44.7506109Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/mimalloc/config	remote.origin.url
2025-03-14T04:12:44.7521377Z Entering 'third_party/nlohmann'
2025-03-14T04:12:44.7566142Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/nlohmann/config	remote.origin.url
2025-03-14T04:12:44.7582071Z Entering 'third_party/onnx'
2025-03-14T04:12:44.7675146Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/config	remote.origin.url
2025-03-14T04:12:44.7676151Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:44.7699462Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/onnx/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:44.7718612Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:44.7761461Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/config	remote.origin.url
2025-03-14T04:12:44.7779573Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:44.7821609Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:44.7839275Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:44.7879417Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.7897754Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:44.7939707Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/ms-gsl/config	remote.origin.url
2025-03-14T04:12:44.7959528Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:44.8000787Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/nlohmann-json/config	remote.origin.url
2025-03-14T04:12:44.8020563Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:44.8061017Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentelemetry-proto/config	remote.origin.url
2025-03-14T04:12:44.8078896Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:44.8129629Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/opentracing-cpp/config	remote.origin.url
2025-03-14T04:12:44.8135364Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:44.8173295Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/config	remote.origin.url
2025-03-14T04:12:44.8191664Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:44.8236034Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/civetweb/config	remote.origin.url
2025-03-14T04:12:44.8254633Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:44.8299202Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/third_party/prometheus-cpp/modules/googletest/config	remote.origin.url
2025-03-14T04:12:44.8316347Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:44.8359376Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/opentelemetry-cpp/modules/tools/vcpkg/config	remote.origin.url
2025-03-14T04:12:44.8392679Z Entering 'third_party/pocketfft'
2025-03-14T04:12:44.8435714Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pocketfft/config	remote.origin.url
2025-03-14T04:12:44.8454133Z Entering 'third_party/protobuf'
2025-03-14T04:12:44.8500882Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/config	remote.origin.url
2025-03-14T04:12:44.8517239Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:44.8558430Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/benchmark/config	remote.origin.url
2025-03-14T04:12:44.8573886Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:44.8616488Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/protobuf/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.8634582Z Entering 'third_party/psimd'
2025-03-14T04:12:44.8674017Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/psimd/config	remote.origin.url
2025-03-14T04:12:44.8690666Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:44.8731151Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/pthreadpool/config	remote.origin.url
2025-03-14T04:12:44.8744733Z Entering 'third_party/pybind11'
2025-03-14T04:12:44.8791569Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:44.8808385Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:44.8855191Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/python-peachpy/config	remote.origin.url
2025-03-14T04:12:44.8863521Z Entering 'third_party/sleef'
2025-03-14T04:12:44.8913583Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/sleef/config	remote.origin.url
2025-03-14T04:12:44.8928142Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:44.8971007Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/config	remote.origin.url
2025-03-14T04:12:44.8984307Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:44.9029622Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/googletest/config	remote.origin.url
2025-03-14T04:12:44.9061495Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:44.9105459Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libnop/config	remote.origin.url
2025-03-14T04:12:44.9121214Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:44.9165677Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/libuv/config	remote.origin.url
2025-03-14T04:12:44.9177937Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:44.9224355Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/config	remote.origin.url
2025-03-14T04:12:44.9236977Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:44.9277176Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/tensorpipe/modules/third_party/pybind11/modules/tools/clang/config	remote.origin.url
2025-03-14T04:12:45.0229468Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:'
2025-03-14T04:12:45.0604841Z Entering 'android/libs/fbjni'
2025-03-14T04:12:45.0643175Z Entering 'third_party/FP16'
2025-03-14T04:12:45.0679017Z Entering 'third_party/FXdiv'
2025-03-14T04:12:45.0717807Z Entering 'third_party/NNPACK'
2025-03-14T04:12:45.0754108Z Entering 'third_party/NVTX'
2025-03-14T04:12:45.0792263Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:45.0833874Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:45.0881006Z Entering 'third_party/benchmark'
2025-03-14T04:12:45.0919400Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:45.0962645Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:45.1000915Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:45.1036962Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:45.1075974Z Entering 'third_party/cutlass'
2025-03-14T04:12:45.1127230Z Entering 'third_party/eigen'
2025-03-14T04:12:45.1166589Z Entering 'third_party/fbgemm'
2025-03-14T04:12:45.1204064Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:45.1239246Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:45.1272294Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:45.1320775Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:45.1357536Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:45.1400924Z Entering 'third_party/flash-attention'
2025-03-14T04:12:45.1438620Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:45.1481142Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:45.1528699Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:45.1567045Z Entering 'third_party/fmt'
2025-03-14T04:12:45.1602513Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:45.1639065Z Entering 'third_party/gloo'
2025-03-14T04:12:45.1673566Z Entering 'third_party/googletest'
2025-03-14T04:12:45.1709982Z Entering 'third_party/ideep'
2025-03-14T04:12:45.1742991Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:45.1786549Z Entering 'third_party/ittapi'
2025-03-14T04:12:45.1822315Z Entering 'third_party/kineto'
2025-03-14T04:12:45.1858851Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:45.1893016Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:45.1929859Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:45.1963953Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:45.1996628Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:45.2031054Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:45.2069867Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:45.2104371Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:45.2135649Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:45.2170090Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:45.2210445Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:45.2247038Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:45.2288434Z Entering 'third_party/kleidiai'
2025-03-14T04:12:45.2326187Z Entering 'third_party/mimalloc'
2025-03-14T04:12:45.2365848Z Entering 'third_party/nlohmann'
2025-03-14T04:12:45.2401529Z Entering 'third_party/onnx'
2025-03-14T04:12:45.2449214Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:45.2490870Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:45.2529964Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:45.2562977Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:45.2599714Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:45.2633593Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:45.2669709Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:45.2703735Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:45.2734380Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:45.2767788Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:45.2807130Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:45.2841349Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:45.2892683Z Entering 'third_party/pocketfft'
2025-03-14T04:12:45.2931174Z Entering 'third_party/protobuf'
2025-03-14T04:12:45.2968640Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:45.3014099Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:45.3056272Z Entering 'third_party/psimd'
2025-03-14T04:12:45.3123773Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:45.3160252Z Entering 'third_party/pybind11'
2025-03-14T04:12:45.3197948Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:45.3233293Z Entering 'third_party/sleef'
2025-03-14T04:12:45.3270741Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:45.3305105Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:45.3341218Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:45.3378730Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:45.3412589Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:45.3446694Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:45.3516214Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:'
2025-03-14T04:12:45.3819913Z Entering 'android/libs/fbjni'
2025-03-14T04:12:45.3866579Z Entering 'third_party/FP16'
2025-03-14T04:12:45.3904840Z Entering 'third_party/FXdiv'
2025-03-14T04:12:45.3942645Z Entering 'third_party/NNPACK'
2025-03-14T04:12:45.3979130Z Entering 'third_party/NVTX'
2025-03-14T04:12:45.4019546Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T04:12:45.4058818Z Entering 'third_party/XNNPACK'
2025-03-14T04:12:45.4109598Z Entering 'third_party/benchmark'
2025-03-14T04:12:45.4148971Z Entering 'third_party/composable_kernel'
2025-03-14T04:12:45.4203545Z Entering 'third_party/cpp-httplib'
2025-03-14T04:12:45.4236296Z Entering 'third_party/cpuinfo'
2025-03-14T04:12:45.4272620Z Entering 'third_party/cudnn_frontend'
2025-03-14T04:12:45.4320793Z Entering 'third_party/cutlass'
2025-03-14T04:12:45.4365011Z Entering 'third_party/eigen'
2025-03-14T04:12:45.4413275Z Entering 'third_party/fbgemm'
2025-03-14T04:12:45.4450193Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T04:12:45.4485829Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T04:12:45.4537858Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T04:12:45.4567184Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T04:12:45.4611585Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T04:12:45.4662385Z Entering 'third_party/flash-attention'
2025-03-14T04:12:45.4702977Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T04:12:45.4751285Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T04:12:45.4791246Z Entering 'third_party/flatbuffers'
2025-03-14T04:12:45.4829939Z Entering 'third_party/fmt'
2025-03-14T04:12:45.4871736Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T04:12:45.4910844Z Entering 'third_party/gloo'
2025-03-14T04:12:45.4946038Z Entering 'third_party/googletest'
2025-03-14T04:12:45.4979356Z Entering 'third_party/ideep'
2025-03-14T04:12:45.5015812Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T04:12:45.5090280Z Entering 'third_party/ittapi'
2025-03-14T04:12:45.5127707Z Entering 'third_party/kineto'
2025-03-14T04:12:45.5163274Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T04:12:45.5200232Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T04:12:45.5233620Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T04:12:45.5270587Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T04:12:45.5307131Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T04:12:45.5337893Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T04:12:45.5376010Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T04:12:45.5416244Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T04:12:45.5453327Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T04:12:45.5490823Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-14T04:12:45.5527381Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T04:12:45.5567667Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T04:12:45.5607094Z Entering 'third_party/kleidiai'
2025-03-14T04:12:45.5641643Z Entering 'third_party/mimalloc'
2025-03-14T04:12:45.5678173Z Entering 'third_party/nlohmann'
2025-03-14T04:12:45.5716955Z Entering 'third_party/onnx'
2025-03-14T04:12:45.5761028Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T04:12:45.5804244Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T04:12:45.5843486Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T04:12:45.5877462Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T04:12:45.5918259Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T04:12:45.5954072Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T04:12:45.5986915Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T04:12:45.6018422Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T04:12:45.6056116Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T04:12:45.6093249Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T04:12:45.6130437Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T04:12:45.6166131Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T04:12:45.6220917Z Entering 'third_party/pocketfft'
2025-03-14T04:12:45.6260758Z Entering 'third_party/protobuf'
2025-03-14T04:12:45.6299530Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T04:12:45.6338522Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T04:12:45.6374769Z Entering 'third_party/psimd'
2025-03-14T04:12:45.6412754Z Entering 'third_party/pthreadpool'
2025-03-14T04:12:45.6447764Z Entering 'third_party/pybind11'
2025-03-14T04:12:45.6483569Z Entering 'third_party/python-peachpy'
2025-03-14T04:12:45.6520372Z Entering 'third_party/sleef'
2025-03-14T04:12:45.6555306Z Entering 'third_party/tensorpipe'
2025-03-14T04:12:45.6597672Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T04:12:45.6646093Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T04:12:45.6681948Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T04:12:45.6719471Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T04:12:45.6764809Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T04:12:45.6817252Z ##[endgroup]
2025-03-14T04:12:45.6859649Z [command]/usr/bin/git log -1 --format=%H
2025-03-14T04:12:45.6874932Z aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:45.7021542Z Prepare all required actions
2025-03-14T04:12:45.7022084Z Getting action download info
2025-03-14T04:12:45.8035780Z ##[group]Run ./.github/actions/setup-linux
2025-03-14T04:12:45.8036015Z env:
2025-03-14T04:12:45.8036190Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:45.8036381Z ##[endgroup]
2025-03-14T04:12:45.8079713Z ##[group]Run set -euo pipefail
2025-03-14T04:12:45.8080007Z set -euo pipefail
2025-03-14T04:12:45.8080384Z function get_ec2_metadata() {
2025-03-14T04:12:45.8080639Z   # Pulled from instance metadata endpoint for EC2
2025-03-14T04:12:45.8081034Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2025-03-14T04:12:45.8081376Z   category=$1
2025-03-14T04:12:45.8081916Z   # If it is GCP runner (runner name contains gcp), do not run this
2025-03-14T04:12:45.8082210Z   runner_name_str=i-047a1559c2de50868
2025-03-14T04:12:45.8082495Z   if [[ -f /.inarc ]]; then
2025-03-14T04:12:45.8082737Z     echo "ARC Runner, no info on ec2 metadata"
2025-03-14T04:12:45.8082995Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2025-03-14T04:12:45.8083302Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2025-03-14T04:12:45.8083567Z   else
2025-03-14T04:12:45.8084082Z     curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}"
2025-03-14T04:12:45.8084587Z   fi
2025-03-14T04:12:45.8084752Z }
2025-03-14T04:12:45.8084944Z echo "ami-id: $(get_ec2_metadata ami-id)"
2025-03-14T04:12:45.8085215Z echo "instance-id: $(get_ec2_metadata instance-id)"
2025-03-14T04:12:45.8085503Z echo "instance-type: $(get_ec2_metadata instance-type)"
2025-03-14T04:12:45.8085763Z echo "system info $(uname -a)"
2025-03-14T04:12:45.8091113Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:45.8091377Z env:
2025-03-14T04:12:45.8091552Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:45.8091738Z ##[endgroup]
2025-03-14T04:12:45.8220334Z ami-id: ami-08b5b3a93ed654d19
2025-03-14T04:12:45.8330700Z instance-id: i-047a1559c2de50868
2025-03-14T04:12:45.8433643Z instance-type: m7i-flex.8xlarge
2025-03-14T04:12:45.8444692Z system info Linux ip-10-0-71-89.ec2.internal 6.1.129-138.220.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Feb 25 22:18:43 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux
2025-03-14T04:12:45.8477018Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:45.8477591Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:12:45.8482215Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:45.8482497Z env:
2025-03-14T04:12:45.8482674Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:45.8482871Z ##[endgroup]
2025-03-14T04:12:45.8540699Z ##[group]Run if systemctl is-active --quiet docker; then
2025-03-14T04:12:45.8541016Z if systemctl is-active --quiet docker; then
2025-03-14T04:12:45.8541272Z     echo "Docker daemon is running...";
2025-03-14T04:12:45.8541499Z else
2025-03-14T04:12:45.8541751Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2025-03-14T04:12:45.8542016Z fi
2025-03-14T04:12:45.8545900Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:45.8546154Z env:
2025-03-14T04:12:45.8546342Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:45.8546534Z ##[endgroup]
2025-03-14T04:12:45.8625736Z Docker daemon is running...
2025-03-14T04:12:45.8667226Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:12:45.8667442Z with:
2025-03-14T04:12:45.8667607Z   shell: bash
2025-03-14T04:12:45.8667935Z   timeout_minutes: 5
2025-03-14T04:12:45.8668121Z   max_attempts: 3
2025-03-14T04:12:45.8668300Z   retry_wait_seconds: 30
2025-03-14T04:12:45.8669559Z   command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\")
aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
    --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"

# For LF Runners we need to make sure we also login to Meta's ECR docker registry too.
META_AWS_ACCOUNT_ID=308535385114
if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then
    aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
        --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
fi

2025-03-14T04:12:45.8670873Z   polling_interval_seconds: 1
2025-03-14T04:12:45.8671075Z   warning_on_retry: true
2025-03-14T04:12:45.8671267Z   continue_on_error: false
2025-03-14T04:12:45.8671452Z env:
2025-03-14T04:12:45.8671617Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:45.8671803Z   AWS_RETRY_MODE: standard
2025-03-14T04:12:45.8671982Z   AWS_MAX_ATTEMPTS: 5
2025-03-14T04:12:45.8672171Z   AWS_DEFAULT_REGION: us-east-1
2025-03-14T04:12:45.8672363Z ##[endgroup]
2025-03-14T04:12:46.8043652Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:46.8048016Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:46.8052132Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:46.8056322Z 
2025-03-14T04:12:46.8058677Z Login Succeeded
2025-03-14T04:12:46.9371941Z Command completed after 1 attempt(s).
2025-03-14T04:12:46.9449113Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:46.9449490Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:46.9449784Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:12:46.9455063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:46.9455321Z env:
2025-03-14T04:12:46.9455498Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.9455696Z ##[endgroup]
2025-03-14T04:12:46.9532701Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:12:46.9533078Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T04:12:46.9533352Z # shellcheck disable=SC2046
2025-03-14T04:12:46.9533605Z docker stop $(docker ps -q) || true
2025-03-14T04:12:46.9533838Z # Prune all of the docker images
2025-03-14T04:12:46.9534062Z docker system prune -af
2025-03-14T04:12:46.9538241Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:46.9538497Z env:
2025-03-14T04:12:46.9538672Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:46.9538864Z ##[endgroup]
2025-03-14T04:12:46.9804215Z "docker stop" requires at least 1 argument.
2025-03-14T04:12:46.9808456Z See 'docker stop --help'.
2025-03-14T04:12:46.9812356Z 
2025-03-14T04:12:46.9816575Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-03-14T04:12:46.9818118Z 
2025-03-14T04:12:46.9818822Z Stop one or more running containers
2025-03-14T04:12:46.9965861Z Total reclaimed space: 0B
2025-03-14T04:12:46.9995352Z ##[group]Run set +e
2025-03-14T04:12:46.9995590Z set +e
2025-03-14T04:12:46.9995768Z set -x
2025-03-14T04:12:46.9995932Z 
2025-03-14T04:12:46.9996133Z PT_DOMAIN=download.pytorch.org
2025-03-14T04:12:46.9996500Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2025-03-14T04:12:46.9996935Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2025-03-14T04:12:46.9997249Z # one is returned at random
2025-03-14T04:12:46.9997510Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2025-03-14T04:12:46.9997754Z 
2025-03-14T04:12:46.9998045Z if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:12:46.9998324Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2025-03-14T04:12:46.9998644Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2025-03-14T04:12:46.9998893Z 
2025-03-14T04:12:46.9999072Z   if [ -z "${RESOLVED_IP}" ]; then
2025-03-14T04:12:46.9999324Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2025-03-14T04:12:46.9999644Z     exit 1
2025-03-14T04:12:46.9999811Z   fi
2025-03-14T04:12:46.9999965Z fi
2025-03-14T04:12:47.0000119Z 
2025-03-14T04:12:47.0000306Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2025-03-14T04:12:47.0000551Z   # Clean up any old records first
2025-03-14T04:12:47.0000786Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2025-03-14T04:12:47.0001000Z fi
2025-03-14T04:12:47.0001152Z 
2025-03-14T04:12:47.0001370Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2025-03-14T04:12:47.0001619Z cat /etc/hosts
2025-03-14T04:12:47.0005900Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.0006153Z env:
2025-03-14T04:12:47.0006327Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.0006515Z ##[endgroup]
2025-03-14T04:12:47.0031205Z + PT_DOMAIN=download.pytorch.org
2025-03-14T04:12:47.0038095Z ++ dig -4 +short download.pytorch.org
2025-03-14T04:12:47.0043134Z ++ tail -n1
2025-03-14T04:12:47.0550872Z + RESOLVED_IP=18.160.10.22
2025-03-14T04:12:47.0555390Z + '[' -z 18.160.10.22 ']'
2025-03-14T04:12:47.0557076Z + grep -r download.pytorch.org /etc/hosts
2025-03-14T04:12:47.0561470Z + sudo sed -i /download.pytorch.org/d /etc/hosts
2025-03-14T04:12:47.0561733Z 18.160.10.76 download.pytorch.org
2025-03-14T04:12:47.3223264Z + echo '18.160.10.22 download.pytorch.org'
2025-03-14T04:12:47.3223666Z + sudo tee -a /etc/hosts
2025-03-14T04:12:47.4083762Z 18.160.10.22 download.pytorch.org
2025-03-14T04:12:47.4102680Z + cat /etc/hosts
2025-03-14T04:12:47.4113358Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2025-03-14T04:12:47.4119687Z ::1         localhost6 localhost6.localdomain6
2025-03-14T04:12:47.4120007Z 18.160.10.22 download.pytorch.org
2025-03-14T04:12:47.4222020Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-03-14T04:12:47.4222318Z with:
2025-03-14T04:12:47.4222785Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4223299Z   docker-build-dir: .ci/docker
2025-03-14T04:12:47.4223500Z   working-directory: .
2025-03-14T04:12:47.4223740Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.4224182Z   force-push: false
2025-03-14T04:12:47.4224391Z env:
2025-03-14T04:12:47.4224578Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.4224789Z ##[endgroup]
2025-03-14T04:12:47.4243592Z ##[group]Run set -ex
2025-03-14T04:12:47.4243839Z set -ex
2025-03-14T04:12:47.4243999Z 
2025-03-14T04:12:47.4244257Z # If the docker build directory or the build script doesn't exist, the action will
2025-03-14T04:12:47.4244702Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-03-14T04:12:47.4245058Z # job could then download the pre-built image as usual
2025-03-14T04:12:47.4245386Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2025-03-14T04:12:47.4245683Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4245977Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4246239Z 
2025-03-14T04:12:47.4246479Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2025-03-14T04:12:47.4246751Z   exit 0
2025-03-14T04:12:47.4246918Z else
2025-03-14T04:12:47.4247107Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4247329Z fi
2025-03-14T04:12:47.4247490Z 
2025-03-14T04:12:47.4247722Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-03-14T04:12:47.4248092Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-03-14T04:12:47.4248425Z   # use it as it is, but first let's extract the tag
2025-03-14T04:12:47.4248838Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-03-14T04:12:47.4249161Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4249467Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4249731Z else
2025-03-14T04:12:47.4249952Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-03-14T04:12:47.4250250Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4250631Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4250966Z fi
2025-03-14T04:12:47.4256557Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.4256810Z env:
2025-03-14T04:12:47.4256985Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.4257195Z   REPO_NAME: pytorch
2025-03-14T04:12:47.4257672Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4258164Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:12:47.4258433Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.4258683Z ##[endgroup]
2025-03-14T04:12:47.4282015Z + [[ ! -d .ci/docker ]]
2025-03-14T04:12:47.4282357Z + [[ ! -f .ci/docker/build.sh ]]
2025-03-14T04:12:47.4282580Z + echo skip=false
2025-03-14T04:12:47.4283241Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849 == *\3\0\8\5\3\5\3\8\5\1\1\4\.\d\k\r\.\e\c\r\.\u\s\-\e\a\s\t\-\1\.\a\m\a\z\o\n\a\w\s\.\c\o\m\/\p\y\t\o\r\c\h* ]]
2025-03-14T04:12:47.4291635Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4292192Z ++ awk -F '[:,]' '{print $2}'
2025-03-14T04:12:47.4310199Z + DOCKER_TAG=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4314959Z + echo docker-tag=aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4320148Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4347553Z ##[group]Run set +e
2025-03-14T04:12:47.4347796Z set +e
2025-03-14T04:12:47.4347973Z set -x
2025-03-14T04:12:47.4348529Z 
2025-03-14T04:12:47.4348706Z login() {
2025-03-14T04:12:47.4349031Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:12:47.4349354Z }
2025-03-14T04:12:47.4349531Z 
2025-03-14T04:12:47.4349691Z retry () {
2025-03-14T04:12:47.4349894Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:12:47.4350121Z }
2025-03-14T04:12:47.4350279Z 
2025-03-14T04:12:47.4350445Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:12:47.4350653Z 
2025-03-14T04:12:47.4350822Z START_TIME=$(date +%s)
2025-03-14T04:12:47.4351036Z # Wait up to 120 minutes
2025-03-14T04:12:47.4351289Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-03-14T04:12:47.4351599Z   # Check if image already exists, if it does then skip building it
2025-03-14T04:12:47.4351911Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-03-14T04:12:47.4352152Z     exit 0
2025-03-14T04:12:47.4352328Z   fi
2025-03-14T04:12:47.4352492Z 
2025-03-14T04:12:47.4352750Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-03-14T04:12:47.4353150Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-03-14T04:12:47.4353635Z   # latter, it will wait for the Docker images to become available before continuing
2025-03-14T04:12:47.4354001Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-03-14T04:12:47.4354282Z     # It's a Docker build job, let's build the image
2025-03-14T04:12:47.4354525Z     break
2025-03-14T04:12:47.4354710Z   else
2025-03-14T04:12:47.4354949Z     # It's a regular build job, wait for the image to become available
2025-03-14T04:12:47.4355221Z     sleep 300
2025-03-14T04:12:47.4355409Z   fi
2025-03-14T04:12:47.4355580Z done
2025-03-14T04:12:47.4355750Z 
2025-03-14T04:12:47.4355997Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-03-14T04:12:47.4356358Z # be empty.  The default action would be to continue rebuild the image
2025-03-14T04:12:47.4356677Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-03-14T04:12:47.4356971Z   # if we're on the base branch then use the parent commit
2025-03-14T04:12:47.4357236Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-03-14T04:12:47.4357450Z else
2025-03-14T04:12:47.4357679Z   # otherwise we're on a PR, so use the most recent base commit
2025-03-14T04:12:47.4357982Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-03-14T04:12:47.4358215Z fi
2025-03-14T04:12:47.4358375Z 
2025-03-14T04:12:47.4358550Z if [[ -z "${MERGE_BASE}" ]]; then
2025-03-14T04:12:47.4358794Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4359013Z 
2025-03-14T04:12:47.4359304Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-03-14T04:12:47.4359692Z   exit 0
2025-03-14T04:12:47.4359858Z fi
2025-03-14T04:12:47.4360015Z 
2025-03-14T04:12:47.4360224Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-03-14T04:12:47.4360627Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-03-14T04:12:47.4360969Z   exit 1
2025-03-14T04:12:47.4361133Z fi
2025-03-14T04:12:47.4361290Z 
2025-03-14T04:12:47.4361526Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-03-14T04:12:47.4361907Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-03-14T04:12:47.4362246Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-03-14T04:12:47.4362638Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-03-14T04:12:47.4363072Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-03-14T04:12:47.4363346Z fi
2025-03-14T04:12:47.4363501Z 
2025-03-14T04:12:47.4363686Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-14T04:12:47.4368337Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:47.4368586Z env:
2025-03-14T04:12:47.4368755Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:47.4368954Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-14T04:12:47.4369193Z   BASE_REVISION: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:12:47.4369720Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4370215Z   DOCKER_TAG: aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:47.4370496Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.4370746Z   DOCKER_PUSH: 
2025-03-14T04:12:47.4370918Z ##[endgroup]
2025-03-14T04:12:47.4395674Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.4399819Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.4404635Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:12:47.4407007Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:47.8768484Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:47.8768851Z Login Succeeded
2025-03-14T04:12:47.8769271Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:47.8769734Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:47.8770256Z 
2025-03-14T04:12:47.8785557Z ++ date +%s
2025-03-14T04:12:47.8794210Z + START_TIME=1741925567
2025-03-14T04:12:47.8799167Z ++ date +%s
2025-03-14T04:12:47.8804531Z + [[ 1741918367 -lt 1741925567 ]]
2025-03-14T04:12:47.8805454Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.1320342Z {
2025-03-14T04:12:48.1320746Z 	"schemaVersion": 2,
2025-03-14T04:12:48.1321107Z 	"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
2025-03-14T04:12:48.1321424Z 	"config": {
2025-03-14T04:12:48.1321669Z 		"mediaType": "application/vnd.docker.container.image.v1+json",
2025-03-14T04:12:48.1321942Z 		"size": 42081,
2025-03-14T04:12:48.1322221Z 		"digest": "sha256:1bab961b3bd90ec6714167a095e5437a0239700971887d1c663377ef708f8526"
2025-03-14T04:12:48.1322518Z 	},
2025-03-14T04:12:48.1322664Z 	"layers": [
2025-03-14T04:12:48.1322827Z 		{
2025-03-14T04:12:48.1323066Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
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2025-03-14T04:12:48.1432251Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.1432518Z 			"size": 106,
2025-03-14T04:12:48.1432864Z 			"digest": "sha256:da83488e1344293ac1fcf6c9ed132b6a3746e8c347dc0c5b4ebc8e24a3b73eee"
2025-03-14T04:12:48.1433171Z 		},
2025-03-14T04:12:48.1433323Z 		{
2025-03-14T04:12:48.1433555Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.1433833Z 			"size": 54145653,
2025-03-14T04:12:48.1434126Z 			"digest": "sha256:78dfb83debe9f57e9eec061eb7ad61e56e2549b77b32b9ca98395ba6655ab167"
2025-03-14T04:12:48.1434437Z 		},
2025-03-14T04:12:48.1434589Z 		{
2025-03-14T04:12:48.1434810Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-14T04:12:48.1435077Z 			"size": 32,
2025-03-14T04:12:48.1435341Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-14T04:12:48.1435625Z 		}
2025-03-14T04:12:48.1435768Z 	]
2025-03-14T04:12:48.1435916Z }
2025-03-14T04:12:48.1436112Z + exit 0
2025-03-14T04:12:48.1466793Z ##[group]Run set -eux
2025-03-14T04:12:48.1467029Z set -eux
2025-03-14T04:12:48.1467577Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin
2025-03-14T04:12:48.1473308Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.1473573Z env:
2025-03-14T04:12:48.1473750Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.1473949Z ##[endgroup]
2025-03-14T04:12:48.1498690Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-03-14T04:12:48.1499059Z + jq --raw-output .SecretString
2025-03-14T04:12:48.1499281Z + jq -r .docker_hub_readonly_token
2025-03-14T04:12:48.1499789Z + docker login --username pytorchbot --password-stdin
2025-03-14T04:12:48.6479173Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:48.6479628Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:48.6480009Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:48.6480306Z 
2025-03-14T04:12:48.6480573Z Login Succeeded
2025-03-14T04:12:48.6560540Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:12:48.6560826Z tag=${ECR_DOCKER_IMAGE##*/}
2025-03-14T04:12:48.6561221Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2025-03-14T04:12:48.6565701Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.6565955Z env:
2025-03-14T04:12:48.6566127Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.6566608Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.6567088Z ##[endgroup]
2025-03-14T04:12:48.6591615Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks-aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.6629667Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-03-14T04:12:48.6629950Z with:
2025-03-14T04:12:48.6630403Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.6630974Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.6631226Z env:
2025-03-14T04:12:48.6631393Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.6631575Z ##[endgroup]
2025-03-14T04:12:48.6840966Z ##[group]Run set -x
2025-03-14T04:12:48.6841188Z set -x
2025-03-14T04:12:48.6841364Z set +e
2025-03-14T04:12:48.6841526Z 
2025-03-14T04:12:48.6841686Z login() {
2025-03-14T04:12:48.6842002Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-14T04:12:48.6842321Z }
2025-03-14T04:12:48.6842484Z 
2025-03-14T04:12:48.6842702Z retry () {
2025-03-14T04:12:48.6842891Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-14T04:12:48.6843106Z }
2025-03-14T04:12:48.6843262Z 
2025-03-14T04:12:48.6843432Z retry login "${DOCKER_REGISTRY}"
2025-03-14T04:12:48.6843643Z 
2025-03-14T04:12:48.6843810Z set -e
2025-03-14T04:12:48.6844046Z # ignore output since only exit code is used for conditional
2025-03-14T04:12:48.6844353Z # only pull docker image if it's not available locally
2025-03-14T04:12:48.6844688Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-03-14T04:12:48.6844997Z   retry docker pull "${DOCKER_IMAGE}"
2025-03-14T04:12:48.6845213Z fi
2025-03-14T04:12:48.6849605Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:12:48.6849852Z env:
2025-03-14T04:12:48.6850028Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:12:48.6850509Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:48.6851041Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.6851292Z ##[endgroup]
2025-03-14T04:12:48.6874039Z + set +e
2025-03-14T04:12:48.6878716Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.6880477Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:48.6880872Z + aws ecr get-login-password --region us-east-1
2025-03-14T04:12:48.6881293Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-14T04:12:49.0901406Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-14T04:12:49.0901802Z Login Succeeded
2025-03-14T04:12:49.0905842Z Configure a credential helper to remove this warning. See
2025-03-14T04:12:49.0906419Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-14T04:12:49.0906741Z 
2025-03-14T04:12:49.0920319Z + set -e
2025-03-14T04:12:49.0922658Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.1077207Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.4053471Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:12:49.4054213Z aa89d6e739080d90fa18625d57297c6734465849: Pulling from pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks
2025-03-14T04:12:49.4054663Z 8f84a9f2102e: Pulling fs layer
2025-03-14T04:12:49.4054882Z f35880ae6ee6: Pulling fs layer
2025-03-14T04:12:49.4055088Z 8eb502731466: Pulling fs layer
2025-03-14T04:12:49.4055615Z d4ce6a1f04ff: Pulling fs layer
2025-03-14T04:12:49.4055822Z ca18d6003a18: Pulling fs layer
2025-03-14T04:12:49.4056019Z f6b56646e2f0: Pulling fs layer
2025-03-14T04:12:49.4056216Z 454c9d6c7abb: Pulling fs layer
2025-03-14T04:12:49.4056416Z a66b8377f49f: Pulling fs layer
2025-03-14T04:12:49.4056618Z c75867b7d634: Pulling fs layer
2025-03-14T04:12:49.4056837Z 64866485623d: Pulling fs layer
2025-03-14T04:12:49.4057033Z 04b678046b3a: Pulling fs layer
2025-03-14T04:12:49.4057226Z 1e2892d1c0d6: Pulling fs layer
2025-03-14T04:12:49.4057423Z 564c8877ceb5: Pulling fs layer
2025-03-14T04:12:49.4057669Z ca18d6003a18: Waiting
2025-03-14T04:12:49.4057891Z f6b56646e2f0: Waiting
2025-03-14T04:12:49.4058068Z 454c9d6c7abb: Waiting
2025-03-14T04:12:49.4058242Z c75867b7d634: Waiting
2025-03-14T04:12:49.4058416Z a66b8377f49f: Waiting
2025-03-14T04:12:49.4058592Z 64866485623d: Waiting
2025-03-14T04:12:49.4058760Z 04b678046b3a: Waiting
2025-03-14T04:12:49.4058937Z 1e2892d1c0d6: Waiting
2025-03-14T04:12:49.4059114Z d4ce6a1f04ff: Waiting
2025-03-14T04:12:49.4059312Z 8d7dafab91e2: Pulling fs layer
2025-03-14T04:12:49.4059514Z 564c8877ceb5: Waiting
2025-03-14T04:12:49.4059703Z 4f4fb700ef54: Pulling fs layer
2025-03-14T04:12:49.4059904Z 3195860f4968: Pulling fs layer
2025-03-14T04:12:49.4060098Z 8d7dafab91e2: Waiting
2025-03-14T04:12:49.4060284Z 64889f83e276: Pulling fs layer
2025-03-14T04:12:49.4060482Z 4f4fb700ef54: Waiting
2025-03-14T04:12:49.4060653Z 3195860f4968: Waiting
2025-03-14T04:12:49.4060834Z a2f45c18f0c1: Pulling fs layer
2025-03-14T04:12:49.4061035Z 16d7101d1441: Pulling fs layer
2025-03-14T04:12:49.4061235Z 9f39c1b701e2: Pulling fs layer
2025-03-14T04:12:49.4061457Z 2edf8a767cf2: Pulling fs layer
2025-03-14T04:12:49.4061649Z 6ee1d4017bc6: Pulling fs layer
2025-03-14T04:12:49.4061840Z 64889f83e276: Waiting
2025-03-14T04:12:49.4062028Z aa5b1d7b7a2c: Pulling fs layer
2025-03-14T04:12:49.4062228Z 6100538713cb: Pulling fs layer
2025-03-14T04:12:49.4062426Z f53dff608124: Pulling fs layer
2025-03-14T04:12:49.4062618Z a2f45c18f0c1: Waiting
2025-03-14T04:12:49.4062805Z 692dab4cd4df: Pulling fs layer
2025-03-14T04:12:49.4063007Z 2e13cb071445: Pulling fs layer
2025-03-14T04:12:49.4063280Z 12fc0b985540: Pulling fs layer
2025-03-14T04:12:49.4063476Z 6100538713cb: Waiting
2025-03-14T04:12:49.4063656Z f53dff608124: Waiting
2025-03-14T04:12:49.4063835Z aa5b1d7b7a2c: Waiting
2025-03-14T04:12:49.4064133Z 692dab4cd4df: Waiting
2025-03-14T04:12:49.4064325Z 2e13cb071445: Waiting
2025-03-14T04:12:49.4064508Z 2edf8a767cf2: Waiting
2025-03-14T04:12:49.4064686Z 6ee1d4017bc6: Waiting
2025-03-14T04:12:49.4064866Z 12fc0b985540: Waiting
2025-03-14T04:12:49.4065047Z 9f39c1b701e2: Waiting
2025-03-14T04:12:49.4065237Z 16d7101d1441: Waiting
2025-03-14T04:12:49.4065420Z 9c0523cdc042: Pulling fs layer
2025-03-14T04:12:49.4065621Z 76be39b43c8f: Pulling fs layer
2025-03-14T04:12:49.4065816Z a813b158a421: Pulling fs layer
2025-03-14T04:12:49.4066009Z 9c0523cdc042: Waiting
2025-03-14T04:12:49.4066186Z 76be39b43c8f: Waiting
2025-03-14T04:12:49.4066379Z bbc30b7542d1: Pulling fs layer
2025-03-14T04:12:49.4066578Z 2a30d35d5515: Pulling fs layer
2025-03-14T04:12:49.4066771Z a813b158a421: Waiting
2025-03-14T04:12:49.4066953Z 34c633845b89: Pulling fs layer
2025-03-14T04:12:49.4067154Z f4328165c8f3: Pulling fs layer
2025-03-14T04:12:49.4067338Z 2a30d35d5515: Waiting
2025-03-14T04:12:49.4067513Z 34c633845b89: Waiting
2025-03-14T04:12:49.4067839Z ced742050257: Pulling fs layer
2025-03-14T04:12:49.4068040Z 7543460794d1: Pulling fs layer
2025-03-14T04:12:49.4068238Z 81f896b48ff0: Pulling fs layer
2025-03-14T04:12:49.4068435Z f11b57697037: Pulling fs layer
2025-03-14T04:12:49.4068633Z d09490ef7bbf: Pulling fs layer
2025-03-14T04:12:49.4068834Z 595c6ea973ce: Pulling fs layer
2025-03-14T04:12:49.4069035Z b65570655a31: Pulling fs layer
2025-03-14T04:12:49.4069234Z f3024011b7b6: Pulling fs layer
2025-03-14T04:12:49.4069433Z 0b7e81f1bc5d: Pulling fs layer
2025-03-14T04:12:49.4069632Z c9603583aaa0: Pulling fs layer
2025-03-14T04:12:49.4069822Z ced742050257: Waiting
2025-03-14T04:12:49.4070077Z 7543460794d1: Waiting
2025-03-14T04:12:49.4070254Z 81f896b48ff0: Waiting
2025-03-14T04:12:49.4070420Z f11b57697037: Waiting
2025-03-14T04:12:49.4070593Z f3024011b7b6: Waiting
2025-03-14T04:12:49.4070770Z 0b7e81f1bc5d: Waiting
2025-03-14T04:12:49.4070972Z 595c6ea973ce: Waiting
2025-03-14T04:12:49.4071149Z c9603583aaa0: Waiting
2025-03-14T04:12:49.4071326Z b65570655a31: Waiting
2025-03-14T04:12:49.4071506Z 68f75179d122: Pulling fs layer
2025-03-14T04:12:49.4071706Z 2ce6ce002394: Pulling fs layer
2025-03-14T04:12:49.4071909Z cb03b7532b5e: Pulling fs layer
2025-03-14T04:12:49.4072110Z 046ab5a4210d: Pulling fs layer
2025-03-14T04:12:49.4072312Z da2b385f2f54: Pulling fs layer
2025-03-14T04:12:49.4072516Z 7bc635713dd5: Pulling fs layer
2025-03-14T04:12:49.4072718Z cd296e79aaba: Pulling fs layer
2025-03-14T04:12:49.4072911Z 046ab5a4210d: Waiting
2025-03-14T04:12:49.4073095Z 26ea03719a99: Pulling fs layer
2025-03-14T04:12:49.4073286Z cb03b7532b5e: Waiting
2025-03-14T04:12:49.4073460Z 68f75179d122: Waiting
2025-03-14T04:12:49.4073646Z f37a0227cb69: Pulling fs layer
2025-03-14T04:12:49.4073833Z 2ce6ce002394: Waiting
2025-03-14T04:12:49.4074014Z da2b385f2f54: Waiting
2025-03-14T04:12:49.4074197Z 0bd4004e84e3: Pulling fs layer
2025-03-14T04:12:49.4074388Z 7bc635713dd5: Waiting
2025-03-14T04:12:49.4074568Z cd296e79aaba: Waiting
2025-03-14T04:12:49.4074752Z 967b86e63131: Pulling fs layer
2025-03-14T04:12:49.4074947Z 26ea03719a99: Waiting
2025-03-14T04:12:49.4075120Z f37a0227cb69: Waiting
2025-03-14T04:12:49.4075305Z 905d662f8eef: Pulling fs layer
2025-03-14T04:12:49.4075501Z 0bd4004e84e3: Waiting
2025-03-14T04:12:49.4075675Z 967b86e63131: Waiting
2025-03-14T04:12:49.4075860Z 39fff80b800e: Pulling fs layer
2025-03-14T04:12:49.4076062Z d681503af97a: Pulling fs layer
2025-03-14T04:12:49.4076261Z acd125116d9b: Pulling fs layer
2025-03-14T04:12:49.4076448Z 39fff80b800e: Waiting
2025-03-14T04:12:49.4076633Z 78b00f805b53: Pulling fs layer
2025-03-14T04:12:49.4076839Z 8b3d8ccc53ec: Pulling fs layer
2025-03-14T04:12:49.4077044Z 07259ecbb7c9: Pulling fs layer
2025-03-14T04:12:49.4077249Z afabfb1e9526: Pulling fs layer
2025-03-14T04:12:49.4077459Z 56aa7cdd744f: Pulling fs layer
2025-03-14T04:12:49.4077656Z 3d413cedb292: Pulling fs layer
2025-03-14T04:12:49.4077853Z c4d93cca7501: Pulling fs layer
2025-03-14T04:12:49.4078049Z be17a58842f0: Pulling fs layer
2025-03-14T04:12:49.4078254Z af209379fbc2: Pulling fs layer
2025-03-14T04:12:49.4078449Z 879b59207c78: Pulling fs layer
2025-03-14T04:12:49.4078640Z 4918b9b279ff: Pulling fs layer
2025-03-14T04:12:49.4078834Z f3e4cb9dda5d: Pulling fs layer
2025-03-14T04:12:49.4079025Z d681503af97a: Waiting
2025-03-14T04:12:49.4079197Z c4d93cca7501: Waiting
2025-03-14T04:12:49.4079363Z afabfb1e9526: Waiting
2025-03-14T04:12:49.4079536Z acd125116d9b: Waiting
2025-03-14T04:12:49.4079709Z be17a58842f0: Waiting
2025-03-14T04:12:49.4079881Z 78b00f805b53: Waiting
2025-03-14T04:12:49.4080052Z af209379fbc2: Waiting
2025-03-14T04:12:49.4080222Z 4918b9b279ff: Waiting
2025-03-14T04:12:49.4080390Z 879b59207c78: Waiting
2025-03-14T04:12:49.4080565Z 8b3d8ccc53ec: Waiting
2025-03-14T04:12:49.4080738Z 56aa7cdd744f: Waiting
2025-03-14T04:12:49.4080911Z f3e4cb9dda5d: Waiting
2025-03-14T04:12:49.4081081Z 3d413cedb292: Waiting
2025-03-14T04:12:49.4081253Z 07259ecbb7c9: Waiting
2025-03-14T04:12:49.4081635Z da83488e1344: Pulling fs layer
2025-03-14T04:12:49.4081859Z 78dfb83debe9: Pulling fs layer
2025-03-14T04:12:49.4082116Z da83488e1344: Waiting
2025-03-14T04:12:49.4082278Z 78dfb83debe9: Waiting
2025-03-14T04:12:49.4965262Z f35880ae6ee6: Verifying Checksum
2025-03-14T04:12:49.4965711Z f35880ae6ee6: Download complete
2025-03-14T04:12:49.5843542Z d4ce6a1f04ff: Verifying Checksum
2025-03-14T04:12:49.5844011Z d4ce6a1f04ff: Download complete
2025-03-14T04:12:49.6610254Z ca18d6003a18: Verifying Checksum
2025-03-14T04:12:49.6615049Z ca18d6003a18: Download complete
2025-03-14T04:12:49.7564391Z f6b56646e2f0: Verifying Checksum
2025-03-14T04:12:49.7564711Z f6b56646e2f0: Download complete
2025-03-14T04:12:49.7666980Z 8f84a9f2102e: Verifying Checksum
2025-03-14T04:12:49.7667571Z 8f84a9f2102e: Download complete
2025-03-14T04:12:49.8374675Z a66b8377f49f: Download complete
2025-03-14T04:12:49.8541003Z 454c9d6c7abb: Download complete
2025-03-14T04:12:49.9519990Z 64866485623d: Verifying Checksum
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2025-03-14T04:12:50.1217210Z 04b678046b3a: Verifying Checksum
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2025-03-14T04:17:33.3980117Z Digest: sha256:2f16eb7d476b5dc359eb789543b0cfc9aa5c04fe105d51acd219f91259bad5ab
2025-03-14T04:17:33.4910066Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:17:33.5345096Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:17:33.5395114Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:17:33.5395703Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-14T04:17:33.5402763Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:33.5403025Z env:
2025-03-14T04:17:33.5403207Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:33.5403405Z ##[endgroup]
2025-03-14T04:17:33.5477734Z Prepare all required actions
2025-03-14T04:17:33.5667957Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-03-14T04:17:33.5668236Z with:
2025-03-14T04:17:33.5668625Z   github-token: ***
2025-03-14T04:17:33.5668816Z env:
2025-03-14T04:17:33.5669007Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:33.5669221Z ##[endgroup]
2025-03-14T04:17:33.5984596Z ##[group]Run set -eux
2025-03-14T04:17:33.5984938Z set -eux
2025-03-14T04:17:33.5985292Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T04:17:33.5990181Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:33.5990483Z env:
2025-03-14T04:17:33.5990676Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:33.5991186Z   GITHUB_TOKEN: ***
2025-03-14T04:17:33.5991391Z ##[endgroup]
2025-03-14T04:17:33.6016030Z + python3 .github/scripts/get_workflow_job_id.py 13849515380 i-047a1559c2de50868
2025-03-14T04:17:34.2151949Z setting job-id=38754841598
2025-03-14T04:17:34.2156537Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:34.2467482Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:17:34.2467960Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-14T04:17:34.2468334Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2025-03-14T04:17:34.2468649Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2025-03-14T04:17:34.2473363Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:34.2473633Z env:
2025-03-14T04:17:34.2473819Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:34.2474023Z   JOB_ID: 38754841598
2025-03-14T04:17:34.2474384Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:34.2474765Z   WORKFLOW_NAME: inductor
2025-03-14T04:17:34.2474971Z   WORKFLOW_RUN_ID: 13849515380
2025-03-14T04:17:34.2475191Z ##[endgroup]
2025-03-14T04:17:34.4188818Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:17:34.4325365Z Requirement already satisfied: psutil==5.9.1 in /home/ec2-user/.local/lib/python3.9/site-packages (5.9.1)
2025-03-14T04:17:34.4326298Z Requirement already satisfied: nvidia-ml-py==11.525.84 in /home/ec2-user/.local/lib/python3.9/site-packages (11.525.84)
2025-03-14T04:17:34.4330740Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7)
2025-03-14T04:17:34.4405455Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0)
2025-03-14T04:17:34.4407010Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1)
2025-03-14T04:17:34.4465487Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (24.2)
2025-03-14T04:17:34.4484754Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.12.2)
2025-03-14T04:17:34.4490709Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.0.0)
2025-03-14T04:17:34.5969154Z Prepare all required actions
2025-03-14T04:17:34.5969482Z Getting action download info
2025-03-14T04:17:34.7416783Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-03-14T04:17:35.6671780Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806)
2025-03-14T04:17:38.6335521Z ##[group]Run ./.github/actions/download-build-artifacts
2025-03-14T04:17:38.6335791Z with:
2025-03-14T04:17:38.6336004Z   name: linux-jammy-py3.9-gcc11-build
2025-03-14T04:17:38.6336236Z   s3-bucket: gha-artifacts
2025-03-14T04:17:38.6336434Z env:
2025-03-14T04:17:38.6336607Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:38.6336802Z ##[endgroup]
2025-03-14T04:17:38.6496262Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:17:38.6496510Z with:
2025-03-14T04:17:38.6496700Z   name: linux-jammy-py3.9-gcc11-build
2025-03-14T04:17:38.6496928Z   s3-bucket: gha-artifacts
2025-03-14T04:17:38.6497187Z   region: us-east-1
2025-03-14T04:17:38.6497364Z env:
2025-03-14T04:17:38.6497535Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:38.6497725Z ##[endgroup]
2025-03-14T04:17:39.2736665Z (node:227990) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:17:39.2738625Z 
2025-03-14T04:17:39.2738994Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:17:39.2739396Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:17:39.2739948Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:17:39.3655924Z Found 1 objects with prefix pytorch/pytorch/13849515380/linux-jammy-py3.9-gcc11-build/
2025-03-14T04:17:39.3659203Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:17:43.4343495Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-14T04:17:43.4349295Z Artifact download has finished successfully
2025-03-14T04:17:43.4529825Z ##[group]Run unzip -o artifacts.zip
2025-03-14T04:17:43.4530083Z unzip -o artifacts.zip
2025-03-14T04:17:43.4534448Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:43.4534706Z env:
2025-03-14T04:17:43.4534876Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:43.4535072Z ##[endgroup]
2025-03-14T04:17:43.4567376Z Archive:  artifacts.zip
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2025-03-14T04:17:49.2207114Z   inflating: build/bin/torch_shm_manager  
2025-03-14T04:17:49.2207567Z    creating: .additional_ci_files/
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2025-03-14T04:17:49.3057963Z ##[group]Run rm artifacts.zip
2025-03-14T04:17:49.3058213Z rm artifacts.zip
2025-03-14T04:17:49.3062715Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:49.3062973Z env:
2025-03-14T04:17:49.3063144Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:49.3063362Z ##[endgroup]
2025-03-14T04:17:49.3619424Z ##[group]Run df -H
2025-03-14T04:17:49.3619649Z df -H
2025-03-14T04:17:49.3624399Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:49.3624671Z env:
2025-03-14T04:17:49.3624853Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:49.3625058Z ##[endgroup]
2025-03-14T04:17:49.3663408Z Filesystem        Size  Used Avail Use% Mounted on
2025-03-14T04:17:49.3663732Z devtmpfs          4.2M     0  4.2M   0% /dev
2025-03-14T04:17:49.3664082Z tmpfs              67G     0   67G   0% /dev/shm
2025-03-14T04:17:49.3664448Z tmpfs              27G  783k   27G   1% /run
2025-03-14T04:17:49.3664687Z /dev/nvme0n1p1    215G   49G  167G  23% /
2025-03-14T04:17:49.3664928Z tmpfs              67G   29k   67G   1% /tmp
2025-03-14T04:17:49.3665172Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2025-03-14T04:17:49.3688957Z Prepare all required actions
2025-03-14T04:17:49.3689293Z Getting action download info
2025-03-14T04:17:49.5064025Z ##[group]Run ./.github/actions/download-td-artifacts
2025-03-14T04:17:49.5064457Z with:
2025-03-14T04:17:49.5064694Z env:
2025-03-14T04:17:49.5064960Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:49.5065222Z ##[endgroup]
2025-03-14T04:17:49.5203026Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-14T04:17:49.5203452Z with:
2025-03-14T04:17:49.5203697Z   name: td_results
2025-03-14T04:17:49.5203955Z   s3-bucket: gha-artifacts
2025-03-14T04:17:49.5204212Z   region: us-east-1
2025-03-14T04:17:49.5204423Z env:
2025-03-14T04:17:49.5204807Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:49.5205066Z ##[endgroup]
2025-03-14T04:17:49.8917035Z (node:228011) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-14T04:17:49.8921308Z 
2025-03-14T04:17:49.8925174Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-14T04:17:49.8926570Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-14T04:17:49.8927005Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-14T04:17:49.9679079Z Found 0 objects with prefix pytorch/pytorch/13849515380/td_results/
2025-03-14T04:17:49.9683669Z Artifact download has finished successfully
2025-03-14T04:17:50.0063584Z ##[group]Run mkdir -p .additional_ci_files
2025-03-14T04:17:50.0095068Z mkdir -p .additional_ci_files
2025-03-14T04:17:50.0095628Z mv td_results.json .additional_ci_files/td_results.json || true
2025-03-14T04:17:50.0100895Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:50.0101189Z env:
2025-03-14T04:17:50.0101380Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.0101580Z ##[endgroup]
2025-03-14T04:17:50.0149049Z mv: cannot stat 'td_results.json': No such file or directory
2025-03-14T04:17:50.0382325Z ##[group]Run .github/scripts/parse_ref.py
2025-03-14T04:17:50.0382639Z .github/scripts/parse_ref.py
2025-03-14T04:17:50.0387683Z shell: /usr/bin/bash -e {0}
2025-03-14T04:17:50.0387897Z env:
2025-03-14T04:17:50.0388073Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.0388265Z ##[endgroup]
2025-03-14T04:17:50.0660772Z Prepare all required actions
2025-03-14T04:17:50.0661356Z Getting action download info
2025-03-14T04:17:50.1862324Z ##[group]Run ./.github/actions/filter-test-configs
2025-03-14T04:17:50.1862585Z with:
2025-03-14T04:17:50.1862990Z   github-token: ***
2025-03-14T04:17:50.1864692Z   test-matrix: {"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}
2025-03-14T04:17:50.1866431Z   job-name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:50.1866782Z env:
2025-03-14T04:17:50.1866964Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.1867164Z ##[endgroup]
2025-03-14T04:17:50.2180151Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T04:17:50.2180390Z with:
2025-03-14T04:17:50.2180559Z   shell: bash
2025-03-14T04:17:50.2180740Z   timeout_minutes: 10
2025-03-14T04:17:50.2180921Z   max_attempts: 5
2025-03-14T04:17:50.2181158Z   retry_wait_seconds: 30
2025-03-14T04:17:50.2181891Z   command: set -eux
# PyYAML 6.0 doesn't work with MacOS x86 anymore
# This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2
python3 -m pip install requests==2.27.1 pyyaml==6.0.1

2025-03-14T04:17:50.2182382Z   polling_interval_seconds: 1
2025-03-14T04:17:50.2182609Z   warning_on_retry: true
2025-03-14T04:17:50.2182824Z   continue_on_error: false
2025-03-14T04:17:50.2183012Z env:
2025-03-14T04:17:50.2183177Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:50.2183589Z   GITHUB_TOKEN: ***
2025-03-14T04:17:50.2183770Z ##[endgroup]
2025-03-14T04:17:50.3035020Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2025-03-14T04:17:50.4724854Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T04:17:50.4852956Z Requirement already satisfied: requests==2.27.1 in /home/ec2-user/.local/lib/python3.9/site-packages (2.27.1)
2025-03-14T04:17:50.4854846Z Requirement already satisfied: pyyaml==6.0.1 in /home/ec2-user/.local/lib/python3.9/site-packages (6.0.1)
2025-03-14T04:17:50.4936676Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2025-03-14T04:17:50.4940102Z Requirement already satisfied: charset-normalizer~=2.0.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2.0.12)
2025-03-14T04:17:50.4945162Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2025-03-14T04:17:50.4951205Z Requirement already satisfied: certifi>=2017.4.17 in /home/ec2-user/.local/lib/python3.9/site-packages (from requests==2.27.1) (2025.1.31)
2025-03-14T04:17:51.2821277Z Command completed after 1 attempt(s).
2025-03-14T04:17:51.3053481Z ##[group]Run set -x
2025-03-14T04:17:51.3053717Z set -x
2025-03-14T04:17:51.3053888Z 
2025-03-14T04:17:51.3054153Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:17:51.3054460Z # in runner workspace
2025-03-14T04:17:51.3054725Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-03-14T04:17:51.3059578Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:51.3059835Z env:
2025-03-14T04:17:51.3060020Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.3060378Z ##[endgroup]
2025-03-14T04:17:51.3120702Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-03-14T04:17:51.3534295Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:17:51.3534605Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-14T04:17:51.3534842Z echo "Job name: ${JOB_NAME}"
2025-03-14T04:17:51.3535051Z 
2025-03-14T04:17:51.3535336Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-14T04:17:51.3535647Z # in runner workspace
2025-03-14T04:17:51.3535930Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-03-14T04:17:51.3536236Z   --workflow "${GITHUB_WORKFLOW}" \
2025-03-14T04:17:51.3536467Z   --job-name "${JOB_NAME}" \
2025-03-14T04:17:51.3538011Z   --test-matrix "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}" \
2025-03-14T04:17:51.3539552Z   --selected-test-configs "" \
2025-03-14T04:17:51.3539791Z   --pr-number "${PR_NUMBER}" \
2025-03-14T04:17:51.3540020Z   --tag "${TAG}" \
2025-03-14T04:17:51.3540239Z   --event-name "${EVENT_NAME}" \
2025-03-14T04:17:51.3540479Z   --schedule "${SCHEDULE}" \
2025-03-14T04:17:51.3540700Z   --branch "${HEAD_BRANCH}"
2025-03-14T04:17:51.3545433Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:51.3545695Z env:
2025-03-14T04:17:51.3545884Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.3546296Z   GITHUB_TOKEN: ***
2025-03-14T04:17:51.3546640Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:51.3547118Z   PR_NUMBER: 
2025-03-14T04:17:51.3547294Z   TAG: 
2025-03-14T04:17:51.3547455Z   EVENT_NAME: push
2025-03-14T04:17:51.3547635Z   SCHEDULE: 
2025-03-14T04:17:51.3547810Z   HEAD_BRANCH: 
2025-03-14T04:17:51.3547990Z ##[endgroup]
2025-03-14T04:17:51.3571983Z Workflow: inductor
2025-03-14T04:17:51.3572546Z Job name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:51.5494958Z ##[group]Run echo "Filtered matrix:"
2025-03-14T04:17:51.5495230Z echo "Filtered matrix:"
2025-03-14T04:17:51.5496712Z echo "{"include": [{"config": "cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "dynamic_cpu_inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.8xlarge.amx"}, {"config": "inductor_torchbench_cpu_smoketest_perf", "shard": 1, "num_shards": 1, "runner": "linux.24xl.spr-metal"}]}"
2025-03-14T04:17:51.5498192Z 
2025-03-14T04:17:51.5498489Z echo
2025-03-14T04:17:51.5498711Z echo "Is the current job unstable? False"
2025-03-14T04:17:51.5498959Z 
2025-03-14T04:17:51.5499129Z echo
2025-03-14T04:17:51.5499342Z echo "Is keep-going label set? False"
2025-03-14T04:17:51.5499579Z 
2025-03-14T04:17:51.5499745Z echo
2025-03-14T04:17:51.5499947Z echo "Renabled issues? "
2025-03-14T04:17:51.5504783Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:51.5505046Z env:
2025-03-14T04:17:51.5505235Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.5505435Z ##[endgroup]
2025-03-14T04:17:51.5597294Z Filtered matrix:
2025-03-14T04:17:51.5601064Z {include: [{config: cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_huggingface, shard: 1, num_shards: 1, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_timm, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 1, num_shards: 2, runner: linux.8xlarge.amx}, {config: dynamic_cpu_inductor_torchbench, shard: 2, num_shards: 2, runner: linux.8xlarge.amx}, {config: inductor_torchbench_cpu_smoketest_perf, shard: 1, num_shards: 1, runner: linux.24xl.spr-metal}]}
2025-03-14T04:17:51.5602480Z 
2025-03-14T04:17:51.5602569Z Is the current job unstable? False
2025-03-14T04:17:51.5602714Z 
2025-03-14T04:17:51.5602797Z Is keep-going label set? False
2025-03-14T04:17:51.5602932Z 
2025-03-14T04:17:51.5603006Z Renabled issues? 
2025-03-14T04:17:51.5811719Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:17:51.5812084Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-14T04:17:51.5816943Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T04:17:51.5817206Z env:
2025-03-14T04:17:51.5817378Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.5817569Z   JOB_TIMEOUT: 240
2025-03-14T04:17:51.5817741Z ##[endgroup]
2025-03-14T04:17:51.6133127Z ##[group]Run set -x
2025-03-14T04:17:51.6133425Z set -x
2025-03-14T04:17:51.6133623Z 
2025-03-14T04:17:51.6134392Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-03-14T04:17:51.6134678Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-03-14T04:17:51.6135134Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-03-14T04:17:51.6135386Z   TEST_COMMAND=.ci/onnx/test.sh
2025-03-14T04:17:51.6135609Z else
2025-03-14T04:17:51.6135807Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:17:51.6136030Z fi
2025-03-14T04:17:51.6136197Z 
2025-03-14T04:17:51.6136398Z # Leaving 1GB for the runner and other things
2025-03-14T04:17:51.6136777Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
2025-03-14T04:17:51.6137366Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
2025-03-14T04:17:51.6137803Z # comes from https://github.com/pytorch/test-infra/pull/6058
2025-03-14T04:17:51.6138144Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
2025-03-14T04:17:51.6138415Z 
2025-03-14T04:17:51.6138624Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:17:51.6138862Z   SHM_OPTS=
2025-03-14T04:17:51.6139054Z   JENKINS_USER=
2025-03-14T04:17:51.6139306Z   # ensure that docker container cleanly exits in 12 hours
2025-03-14T04:17:51.6139617Z   # if for some reason cleanup action doesn't stop container
2025-03-14T04:17:51.6139884Z   # when job is cancelled
2025-03-14T04:17:51.6140111Z   DOCKER_SHELL_CMD="sleep 12h"
2025-03-14T04:17:51.6140331Z 
2025-03-14T04:17:51.6140579Z   # since some steps are skipped on s390x, if they are necessary, run them here
2025-03-14T04:17:51.6140923Z   env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:17:51.6141217Z   env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-14T04:17:51.6141460Z else
2025-03-14T04:17:51.6141656Z   SHM_OPTS="--shm-size=${SHM_SIZE}"
2025-03-14T04:17:51.6142561Z   JENKINS_USER="--user jenkins"
2025-03-14T04:17:51.6142799Z   DOCKER_SHELL_CMD=
2025-03-14T04:17:51.6142998Z fi
2025-03-14T04:17:51.6143166Z 
2025-03-14T04:17:51.6143408Z # detached container should get cleaned up by teardown_ec2_linux
2025-03-14T04:17:51.6143750Z # TODO: Stop building test binaries as part of the build phase
2025-03-14T04:17:51.6144191Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
2025-03-14T04:17:51.6144544Z # shellcheck disable=SC2086,SC2090
2025-03-14T04:17:51.6144784Z container_name=$(docker run \
2025-03-14T04:17:51.6145013Z   ${GPU_FLAG:-} \
2025-03-14T04:17:51.6145249Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2025-03-14T04:17:51.6145504Z   -e BUILD_ENVIRONMENT \
2025-03-14T04:17:51.6145714Z   -e PR_NUMBER \
2025-03-14T04:17:51.6145914Z   -e GITHUB_ACTIONS \
2025-03-14T04:17:51.6146119Z   -e GITHUB_REPOSITORY \
2025-03-14T04:17:51.6146327Z   -e GITHUB_WORKFLOW \
2025-03-14T04:17:51.6146529Z   -e GITHUB_JOB \
2025-03-14T04:17:51.6146724Z   -e GITHUB_RUN_ID \
2025-03-14T04:17:51.6146925Z   -e GITHUB_RUN_NUMBER \
2025-03-14T04:17:51.6147131Z   -e GITHUB_RUN_ATTEMPT \
2025-03-14T04:17:51.6147347Z   -e JOB_ID \
2025-03-14T04:17:51.6147548Z   -e JOB_NAME \
2025-03-14T04:17:51.6147747Z   -e BASE_SHA \
2025-03-14T04:17:51.6147944Z   -e BRANCH \
2025-03-14T04:17:51.6148138Z   -e SHA1 \
2025-03-14T04:17:51.6148339Z   -e AWS_DEFAULT_REGION \
2025-03-14T04:17:51.6148683Z   -e IN_WHEEL_TEST \
2025-03-14T04:17:51.6148899Z   -e SHARD_NUMBER \
2025-03-14T04:17:51.6149109Z   -e TEST_CONFIG \
2025-03-14T04:17:51.6149322Z   -e NUM_TEST_SHARDS \
2025-03-14T04:17:51.6149543Z   -e REENABLED_ISSUES \
2025-03-14T04:17:51.6149825Z   -e CONTINUE_THROUGH_ERROR \
2025-03-14T04:17:51.6150043Z   -e VERBOSE_TEST_LOGS \
2025-03-14T04:17:51.6150258Z   -e TEST_SHOWLOCALS \
2025-03-14T04:17:51.6150469Z   -e NO_TEST_TIMEOUT \
2025-03-14T04:17:51.6150675Z   -e NO_TD \
2025-03-14T04:17:51.6150875Z   -e TD_DISTRIBUTED \
2025-03-14T04:17:51.6151086Z   -e PR_LABELS \
2025-03-14T04:17:51.6151314Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-03-14T04:17:51.6151560Z   -e SCCACHE_BUCKET \
2025-03-14T04:17:51.6151772Z   -e SCCACHE_REGION \
2025-03-14T04:17:51.6151977Z   -e XLA_CUDA \
2025-03-14T04:17:51.6152198Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2025-03-14T04:17:51.6152497Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-03-14T04:17:51.6152752Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-03-14T04:17:51.6153011Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2025-03-14T04:17:51.6153257Z   -e HUGGING_FACE_HUB_TOKEN \
2025-03-14T04:17:51.6153502Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2025-03-14T04:17:51.6153737Z   -e DASHBOARD_TAG \
2025-03-14T04:17:51.6153946Z   -e IS_A100_RUNNER \
2025-03-14T04:17:51.6154162Z   -e ARTIFACTS_FILE_SUFFIX \
2025-03-14T04:17:51.6154421Z   --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
2025-03-14T04:17:51.6154713Z   --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
2025-03-14T04:17:51.6155005Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2025-03-14T04:17:51.6155289Z   --security-opt seccomp=unconfined \
2025-03-14T04:17:51.6155535Z   --cap-add=SYS_PTRACE \
2025-03-14T04:17:51.6155759Z   --ipc=host \
2025-03-14T04:17:51.6155959Z   ${SHM_OPTS} \
2025-03-14T04:17:51.6156154Z   --tty \
2025-03-14T04:17:51.6156341Z   --detach \
2025-03-14T04:17:51.6156548Z   --name="${container_name}" \
2025-03-14T04:17:51.6156775Z   ${JENKINS_USER} \
2025-03-14T04:17:51.6157021Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-03-14T04:17:51.6157284Z   -w /var/lib/jenkins/workspace \
2025-03-14T04:17:51.6157504Z   "${DOCKER_IMAGE}" \
2025-03-14T04:17:51.6157694Z   ${DOCKER_SHELL_CMD}
2025-03-14T04:17:51.6157882Z )
2025-03-14T04:17:51.6158091Z # Propagate download.pytorch.org IP to container
2025-03-14T04:17:51.6158498Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2025-03-14T04:17:51.6158927Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2025-03-14T04:17:51.6159193Z 
2025-03-14T04:17:51.6159390Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-14T04:17:51.6159745Z   docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt"
2025-03-14T04:17:51.6160066Z fi
2025-03-14T04:17:51.6160232Z 
2025-03-14T04:17:51.6160544Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2025-03-14T04:17:51.6165247Z shell: /usr/bin/bash -e {0}
2025-03-14T04:17:51.6165456Z env:
2025-03-14T04:17:51.6165647Z   GIT_DEFAULT_BRANCH: main
2025-03-14T04:17:51.6165880Z   BUILD_ENVIRONMENT: linux-jammy-py3.9-gcc11-build
2025-03-14T04:17:51.6166122Z   PR_NUMBER: 
2025-03-14T04:17:51.6166308Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-03-14T04:17:51.6166533Z   GITHUB_WORKFLOW: inductor
2025-03-14T04:17:51.6166728Z   GITHUB_JOB: test
2025-03-14T04:17:51.6166909Z   GITHUB_RUN_ID: 13849515380
2025-03-14T04:17:51.6167176Z   GITHUB_RUN_NUMBER: 122697
2025-03-14T04:17:51.6167375Z   GITHUB_RUN_ATTEMPT: 1
2025-03-14T04:17:51.6167562Z   JOB_ID: 38754841598
2025-03-14T04:17:51.6167902Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:17:51.6168251Z   BRANCH: main
2025-03-14T04:17:51.6168496Z   SHA1: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:17:51.6168753Z   BASE_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:17:51.6168990Z   TEST_CONFIG: cpu_inductor_torchbench
2025-03-14T04:17:51.6169204Z   SHARD_NUMBER: 1
2025-03-14T04:17:51.6169378Z   NUM_TEST_SHARDS: 2
2025-03-14T04:17:51.6169560Z   REENABLED_ISSUES: 
2025-03-14T04:17:51.6169747Z   CONTINUE_THROUGH_ERROR: False
2025-03-14T04:17:51.6169950Z   VERBOSE_TEST_LOGS: False
2025-03-14T04:17:51.6170143Z   TEST_SHOWLOCALS: False
2025-03-14T04:17:51.6170331Z   NO_TEST_TIMEOUT: False
2025-03-14T04:17:51.6170514Z   NO_TD: False
2025-03-14T04:17:51.6170682Z   TD_DISTRIBUTED: False
2025-03-14T04:17:51.6170905Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2025-03-14T04:17:51.6171148Z   SCCACHE_REGION: us-east-1
2025-03-14T04:17:51.6171341Z   SHM_SIZE: 1g
2025-03-14T04:17:51.6171799Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:17:51.6172278Z   XLA_CUDA: 
2025-03-14T04:17:51.6172525Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:17:51.6172831Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-03-14T04:17:51.6173056Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-03-14T04:17:51.6173262Z   DASHBOARD_TAG: 
2025-03-14T04:17:51.6173674Z   HUGGING_FACE_HUB_TOKEN: ***
2025-03-14T04:17:51.6173956Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2025-03-14T04:17:51.6174168Z   IS_A100_RUNNER: 0
2025-03-14T04:17:51.6174454Z   ARTIFACTS_FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T04:17:51.6174769Z ##[endgroup]
2025-03-14T04:17:51.6201416Z + [[ cpu_inductor_torchbench == \m\u\l\t\i\g\p\u ]]
2025-03-14T04:17:51.6206021Z + [[ linux-jammy-py3.9-gcc11-build == *onnx* ]]
2025-03-14T04:17:51.6210953Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-03-14T04:17:51.6215724Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo
2025-03-14T04:17:51.6220793Z + TOTAL_AVAILABLE_MEMORY_IN_GB='122.780 '
2025-03-14T04:17:51.6221089Z + TOTAL_MEMORY_WITH_SWAP=125
2025-03-14T04:17:51.6221344Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]]
2025-03-14T04:17:51.6221603Z + SHM_OPTS=--shm-size=1g
2025-03-14T04:17:51.6221812Z + JENKINS_USER='--user jenkins'
2025-03-14T04:17:51.6222015Z + DOCKER_SHELL_CMD=
2025-03-14T04:17:51.6229775Z +++ nproc --ignore=2
2025-03-14T04:17:51.6240081Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=30 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=122g --memory-swap=125g --env-file=/tmp/github_env_13849515380 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T04:18:46.5771540Z + container_name=2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T04:18:46.5772009Z + grep download.pytorch.org /etc/hosts
2025-03-14T04:18:46.5775546Z + docker exec -i 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231 sudo bash -c '/bin/cat >> /etc/hosts'
2025-03-14T04:18:46.6777770Z + echo DOCKER_CONTAINER_ID=2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T04:18:46.6778296Z + [[ linux-jammy-py3.9-gcc11-build == *\s\3\9\0\x* ]]
2025-03-14T04:18:46.6780604Z ++ echo dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl
2025-03-14T04:18:46.6788486Z + docker exec -t 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231 sh -c 'python3 -m pip install dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2025-03-14T04:18:47.0767442Z Processing ./dist/torch-2.8.0a0+gitaed0b7a-cp39-cp39-linux_x86_64.whl (from torch==2.8.0a0+gitaed0b7a)
2025-03-14T04:18:47.2553863Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.16.1)
2025-03-14T04:18:47.2554674Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (4.12.2)
2025-03-14T04:18:47.2746717Z Collecting sympy>=1.13.3 (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a)
2025-03-14T04:18:47.2765457Z   Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB)
2025-03-14T04:18:47.2775700Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2.8.8)
2025-03-14T04:18:47.2776723Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.1.6)
2025-03-14T04:18:47.2777738Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (2024.10.0)
2025-03-14T04:18:47.2796203Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.3.0)
2025-03-14T04:18:47.2812474Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opt-einsum>=3.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.22.4)
2025-03-14T04:18:47.2818012Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (1.3.0)
2025-03-14T04:18:47.3093250Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch==2.8.0a0+gitaed0b7a->torch==2.8.0a0+gitaed0b7a) (3.0.2)
2025-03-14T04:18:47.3160647Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB)
2025-03-14T04:18:47.8326600Z Installing collected packages: sympy, torch
2025-03-14T04:18:47.8328684Z   Attempting uninstall: sympy
2025-03-14T04:18:47.8338411Z     Found existing installation: sympy 1.13.1
2025-03-14T04:18:47.9236188Z     Uninstalling sympy-1.13.1:
2025-03-14T04:18:48.4105566Z       Successfully uninstalled sympy-1.13.1
2025-03-14T04:18:57.6218543Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
2025-03-14T04:18:57.6219338Z timm 1.0.14 requires torchvision, which is not installed.
2025-03-14T04:18:57.6219781Z Successfully installed sympy-1.13.3 torch-2.8.0a0+gitaed0b7a
2025-03-14T04:18:57.7132278Z + export TERM=vt100
2025-03-14T04:18:57.7132548Z + TERM=vt100
2025-03-14T04:18:57.7132747Z ++ dirname .ci/pytorch/test.sh
2025-03-14T04:18:57.7141446Z + source .ci/pytorch/common.sh
2025-03-14T04:18:57.7141757Z +++ dirname .ci/pytorch/common.sh
2025-03-14T04:18:57.7153935Z ++ source .ci/pytorch/common_utils.sh
2025-03-14T04:18:57.7157630Z +++ declare -f -t trap_add
2025-03-14T04:18:57.7157882Z ++ set -ex -o pipefail
2025-03-14T04:18:57.7158113Z ++ [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:18:57.7158364Z ++ BUILD_TEST_LIBTORCH=0
2025-03-14T04:18:57.7158592Z + [[ linux-jammy-py3.9-gcc11-build != *rocm* ]]
2025-03-14T04:18:57.7159129Z + [[ linux-jammy-py3.9-gcc11-build != *s390x* ]]
2025-03-14T04:18:57.7159380Z + [[ -d /var/lib/jenkins/workspace ]]
2025-03-14T04:18:57.7159616Z ++ stat -c %u /var/lib/jenkins/workspace
2025-03-14T04:18:57.7168640Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2025-03-14T04:18:57.7168924Z + trap_add cleanup_workspace EXIT
2025-03-14T04:18:57.7169164Z + trap_add_cmd=cleanup_workspace
2025-03-14T04:18:57.7169375Z + shift
2025-03-14T04:18:57.7169550Z + for trap_add_name in "$@"
2025-03-14T04:18:57.7175414Z +++ trap -p EXIT
2025-03-14T04:18:57.7175772Z ++ eval 'extract_trap_cmd '
2025-03-14T04:18:57.7176101Z +++ extract_trap_cmd
2025-03-14T04:18:57.7176350Z +++ printf '%s\n' ''
2025-03-14T04:18:57.7177898Z ++ printf '%s\n' cleanup_workspace
2025-03-14T04:18:57.7178284Z + trap -- '
2025-03-14T04:18:57.7178503Z cleanup_workspace' EXIT
2025-03-14T04:18:57.7178760Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2025-03-14T04:18:58.0651208Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2025-03-14T04:18:58.0669723Z + echo 'Environment variables:'
2025-03-14T04:18:58.0689944Z Environment variables:
2025-03-14T04:18:58.0691942Z + env
2025-03-14T04:18:58.0692356Z INSTALLED_DB=yes
2025-03-14T04:18:58.0692731Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:18:58.0693049Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:18:58.0693315Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build
2025-03-14T04:18:58.0693556Z HOSTNAME=2160b5b633d5
2025-03-14T04:18:58.0693924Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0694297Z GITHUB_ACTION=__self
2025-03-14T04:18:58.0694525Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:18:58.0694742Z GITHUB_RUN_NUMBER=122697
2025-03-14T04:18:58.0694945Z TEST_CONFIG=cpu_inductor_torchbench
2025-03-14T04:18:58.0695159Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:18:58.0695389Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:18:58.0695613Z IS_A100_RUNNER=0
2025-03-14T04:18:58.0696008Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:18:58.0696230Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:18:58.0696447Z GITHUB_REF_TYPE=branch
2025-03-14T04:18:58.0696641Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:18:58.0696870Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0697111Z XLA_CUDA=
2025-03-14T04:18:58.0697347Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:18:58.0704532Z ***
2025-03-14T04:18:58.0704830Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:18:58.0705063Z GITHUB_ACTIONS=true
2025-03-14T04:18:58.0705277Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0705569Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0705920Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/heads/main
2025-03-14T04:18:58.0706234Z UCC_HOME=/usr
2025-03-14T04:18:58.0706418Z VERBOSE_TEST_LOGS=False
2025-03-14T04:18:58.0706619Z GITHUB_REF=refs/heads/main
2025-03-14T04:18:58.0706823Z SHARD_NUMBER=1
2025-03-14T04:18:58.0707001Z GITHUB_REF_PROTECTED=true
2025-03-14T04:18:58.0707201Z HOME=/var/lib/jenkins
2025-03-14T04:18:58.0707420Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:18:58.0707665Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:18:58.0707880Z UCX_COMMIT=
2025-03-14T04:18:58.0708049Z NUM_TEST_SHARDS=2
2025-03-14T04:18:58.0708227Z UCX_HOME=/usr
2025-03-14T04:18:58.0708599Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0709146Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:58.0709885Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0710383Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:18:58.0710702Z GITHUB_EVENT_NAME=push
2025-03-14T04:18:58.0710997Z DASHBOARD_TAG=
2025-03-14T04:18:58.0711180Z GITHUB_RUN_ID=13849515380
2025-03-14T04:18:58.0711582Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0712000Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:18:58.0712205Z PR_NUMBER=
2025-03-14T04:18:58.0712364Z DESIRED_CUDA=
2025-03-14T04:18:58.0712544Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:18:58.0712742Z ANACONDA_PYTHON_VERSION=3.9
2025-03-14T04:18:58.0712985Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:18:58.0713226Z TERM=vt100
2025-03-14T04:18:58.0713394Z INSTALLED_VISION=yes
2025-03-14T04:18:58.0713578Z BRANCH=main
2025-03-14T04:18:58.0713755Z SCCACHE_REGION=us-east-1
2025-03-14T04:18:58.0713965Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:18:58.0714176Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:18:58.0714513Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:18:58.0714883Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:18:58.0715104Z UCC_COMMIT=
2025-03-14T04:18:58.0715274Z REENABLED_ISSUES=
2025-03-14T04:18:58.0715444Z DOCS=yes
2025-03-14T04:18:58.0715609Z SHLVL=1
2025-03-14T04:18:58.0715770Z MAX_JOBS=30
2025-03-14T04:18:58.0715942Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:18:58.0716188Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0716527Z GITHUB_REF_NAME=main
2025-03-14T04:18:58.0716795Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:18:58.0717078Z GITHUB_JOB=test
2025-03-14T04:18:58.0717258Z NO_TEST_TIMEOUT=False
2025-03-14T04:18:58.0717447Z TD_DISTRIBUTED=False
2025-03-14T04:18:58.0717651Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:18:58.0717870Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:18:58.0718069Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:18:58.0718264Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:18:58.0718743Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:18:58.0719220Z GITHUB_BASE_REF=
2025-03-14T04:18:58.0719387Z INSTALLED_ACL=
2025-03-14T04:18:58.0719661Z ARTIFACTS_FILE_SUFFIX=test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T04:18:58.0719969Z CI=true
2025-03-14T04:18:58.0720139Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:18:58.0720339Z JOB_ID=38754841598
2025-03-14T04:18:58.0720515Z INSTALLED_PROTOBUF=yes
2025-03-14T04:18:58.0720693Z GITHUB_HEAD_REF=
2025-03-14T04:18:58.0720867Z GITHUB_ACTION_REF=
2025-03-14T04:18:58.0721074Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:18:58.0721310Z TEST_SHOWLOCALS=False
2025-03-14T04:18:58.0721496Z GITHUB_WORKFLOW=inductor
2025-03-14T04:18:58.0721693Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:18:58.0722072Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0722450Z NO_TD=False
2025-03-14T04:18:58.0722624Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:18:58.0722820Z _=/usr/bin/env
2025-03-14T04:18:58.0723041Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-03-14T04:18:58.0925134Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch
2025-03-14T04:18:58.0925734Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/bin
2025-03-14T04:18:58.0926179Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/lib
2025-03-14T04:18:58.0927182Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/test
2025-03-14T04:18:58.0927545Z + BUILD_DIR=build
2025-03-14T04:18:58.0928116Z + BUILD_RENAMED_DIR=build_renamed
2025-03-14T04:18:58.0928358Z + BUILD_BIN_DIR=build/bin
2025-03-14T04:18:58.0928565Z + SHARD_NUMBER=1
2025-03-14T04:18:58.0928748Z + NUM_TEST_SHARDS=2
2025-03-14T04:18:58.0928960Z + export TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:18:58.0929190Z + TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:18:58.0929550Z + export VALGRIND=ON
2025-03-14T04:18:58.0929741Z + VALGRIND=ON
2025-03-14T04:18:58.0929956Z + [[ linux-jammy-py3.9-gcc11-build == *clang9* ]]
2025-03-14T04:18:58.0930219Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:18:58.0930476Z + [[ linux-jammy-py3.9-gcc11-build == *s390x* ]]
2025-03-14T04:18:58.0930707Z + [[ 0 == \1 ]]
2025-03-14T04:18:58.0930887Z + [[ False == \1 ]]
2025-03-14T04:18:58.0931091Z + [[ linux-jammy-py3.9-gcc11-build != *bazel* ]]
2025-03-14T04:18:58.0938824Z ++ realpath build/custom_test_artifacts
2025-03-14T04:18:58.0949022Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2025-03-14T04:18:58.0949555Z + [[ -n '' ]]
2025-03-14T04:18:58.0949943Z + echo 'Environment variables'
2025-03-14T04:18:58.0950226Z Environment variables
2025-03-14T04:18:58.0950727Z + env
2025-03-14T04:18:58.0955554Z INSTALLED_DB=yes
2025-03-14T04:18:58.0955984Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T04:18:58.0956394Z CONTINUE_THROUGH_ERROR=False
2025-03-14T04:18:58.0957253Z BUILD_ENVIRONMENT=linux-jammy-py3.9-gcc11-build
2025-03-14T04:18:58.0957564Z HOSTNAME=2160b5b633d5
2025-03-14T04:18:58.0958017Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0958463Z GITHUB_ACTION=__self
2025-03-14T04:18:58.0958681Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-14T04:18:58.0958912Z GITHUB_RUN_NUMBER=122697
2025-03-14T04:18:58.0959130Z TEST_CONFIG=cpu_inductor_torchbench
2025-03-14T04:18:58.0959357Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-14T04:18:58.0959599Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-14T04:18:58.0959854Z IS_A100_RUNNER=0
2025-03-14T04:18:58.0960345Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-14T04:18:58.0960582Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot
2025-03-14T04:18:58.0960811Z GITHUB_REF_TYPE=branch
2025-03-14T04:18:58.0961010Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-14T04:18:58.0961246Z BASE_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0961496Z XLA_CUDA=
2025-03-14T04:18:58.0961740Z HUGGING_FACE_HUB_TOKEN=***
2025-03-14T04:18:58.0962160Z ***
2025-03-14T04:18:58.0962340Z GITHUB_REPOSITORY_ID=65600975
2025-03-14T04:18:58.0962549Z GITHUB_ACTIONS=true
2025-03-14T04:18:58.0962757Z SHA1=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0963018Z GITHUB_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0963362Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/heads/main
2025-03-14T04:18:58.0963677Z UCC_HOME=/usr
2025-03-14T04:18:58.0963860Z TORCH_SERIALIZATION_DEBUG=1
2025-03-14T04:18:58.0964066Z VERBOSE_TEST_LOGS=False
2025-03-14T04:18:58.0964268Z GITHUB_REF=refs/heads/main
2025-03-14T04:18:58.0964451Z SHARD_NUMBER=1
2025-03-14T04:18:58.0964632Z GITHUB_REF_PROTECTED=true
2025-03-14T04:18:58.0964831Z HOME=/var/lib/jenkins
2025-03-14T04:18:58.0965043Z GITHUB_API_URL=https://api.github.com
2025-03-14T04:18:58.0965275Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-14T04:18:58.0965490Z UCX_COMMIT=
2025-03-14T04:18:58.0965654Z NUM_TEST_SHARDS=2
2025-03-14T04:18:58.0965828Z UCX_HOME=/usr
2025-03-14T04:18:58.0966193Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0966734Z JOB_NAME=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T04:18:58.0967260Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0967744Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-14T04:18:58.0968248Z GITHUB_EVENT_NAME=push
2025-03-14T04:18:58.0968450Z DASHBOARD_TAG=
2025-03-14T04:18:58.0968632Z GITHUB_RUN_ID=13849515380
2025-03-14T04:18:58.0969032Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0969510Z GITHUB_ACTOR=pytorchmergebot
2025-03-14T04:18:58.0969713Z PR_NUMBER=
2025-03-14T04:18:58.0969879Z DESIRED_CUDA=
2025-03-14T04:18:58.0970045Z GITHUB_RUN_ATTEMPT=1
2025-03-14T04:18:58.0970232Z VALGRIND=ON
2025-03-14T04:18:58.0970410Z ANACONDA_PYTHON_VERSION=3.9
2025-03-14T04:18:58.0970654Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-14T04:18:58.0970892Z TERM=vt100
2025-03-14T04:18:58.0971061Z INSTALLED_VISION=yes
2025-03-14T04:18:58.0971240Z BRANCH=main
2025-03-14T04:18:58.0971416Z SCCACHE_REGION=us-east-1
2025-03-14T04:18:58.0971620Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-14T04:18:58.0971829Z CUDA_PATH=/usr/local/cuda
2025-03-14T04:18:58.0972168Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-14T04:18:58.0972529Z GITHUB_SERVER_URL=https://github.com
2025-03-14T04:18:58.0972743Z UCC_COMMIT=
2025-03-14T04:18:58.0972912Z REENABLED_ISSUES=
2025-03-14T04:18:58.0973091Z DOCS=yes
2025-03-14T04:18:58.0973245Z SHLVL=1
2025-03-14T04:18:58.0973412Z MAX_JOBS=30
2025-03-14T04:18:58.0973586Z GITHUB_ACTOR_ID=97764156
2025-03-14T04:18:58.0973851Z GITHUB_WORKFLOW_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T04:18:58.0974120Z GITHUB_REF_NAME=main
2025-03-14T04:18:58.0974390Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-14T04:18:58.0974678Z GITHUB_JOB=test
2025-03-14T04:18:58.0974855Z NO_TEST_TIMEOUT=False
2025-03-14T04:18:58.0975044Z TD_DISTRIBUTED=False
2025-03-14T04:18:58.0975243Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-14T04:18:58.0975465Z GITHUB_RETENTION_DAYS=90
2025-03-14T04:18:58.0975662Z OPENSSL_DIR=/opt/openssl
2025-03-14T04:18:58.0975862Z GITHUB_ACTION_REPOSITORY=
2025-03-14T04:18:58.0976340Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:18:58.0976809Z GITHUB_BASE_REF=
2025-03-14T04:18:58.0976989Z INSTALLED_ACL=
2025-03-14T04:18:58.0977269Z ARTIFACTS_FILE_SUFFIX=test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T04:18:58.0977581Z CI=true
2025-03-14T04:18:58.0977758Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-14T04:18:58.0977956Z JOB_ID=38754841598
2025-03-14T04:18:58.0978136Z INSTALLED_PROTOBUF=yes
2025-03-14T04:18:58.0978324Z GITHUB_HEAD_REF=
2025-03-14T04:18:58.0978501Z GITHUB_ACTION_REF=
2025-03-14T04:18:58.0978715Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-14T04:18:58.0978959Z TEST_SHOWLOCALS=False
2025-03-14T04:18:58.0979146Z GITHUB_WORKFLOW=inductor
2025-03-14T04:18:58.0979348Z DEBIAN_FRONTEND=noninteractive
2025-03-14T04:18:58.0979742Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_eec2a1dd-795a-4a1d-ade5-363f790577cc
2025-03-14T04:18:58.0980134Z NO_TD=False
2025-03-14T04:18:58.0980312Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-14T04:18:58.0980516Z _=/usr/bin/env
2025-03-14T04:18:58.0980695Z + echo 'Testing pytorch'
2025-03-14T04:18:58.0980884Z Testing pytorch
2025-03-14T04:18:58.0981098Z + export LANG=C.UTF-8
2025-03-14T04:18:58.0981278Z + LANG=C.UTF-8
2025-03-14T04:18:58.0981641Z + PR_NUMBER=
2025-03-14T04:18:58.0981870Z + [[ cpu_inductor_torchbench == \d\e\f\a\u\l\t ]]
2025-03-14T04:18:58.0982150Z + [[ cpu_inductor_torchbench == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-03-14T04:18:58.0982417Z + [[ cpu_inductor_torchbench == \s\l\o\w ]]
2025-03-14T04:18:58.0982702Z + [[ linux-jammy-py3.9-gcc11-build == *slow-gradcheck* ]]
2025-03-14T04:18:58.0982982Z + [[ linux-jammy-py3.9-gcc11-build == *cuda* ]]
2025-03-14T04:18:58.0983240Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:18:58.0983495Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:18:58.0983886Z + [[ cpu_inductor_torchbench == *crossref* ]]
2025-03-14T04:18:58.0984151Z + [[ linux-jammy-py3.9-gcc11-build == *rocm* ]]
2025-03-14T04:18:58.0984521Z + [[ linux-jammy-py3.9-gcc11-build == *xpu* ]]
2025-03-14T04:18:58.0984795Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]]
2025-03-14T04:18:58.0985067Z + pip_install --user ninja==1.10.2
2025-03-14T04:18:58.0985464Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:18:58.0985808Z + python3 -m pip install --progress-bar off --user ninja==1.10.2
2025-03-14T04:18:58.4712011Z Collecting ninja==1.10.2
2025-03-14T04:18:58.4851514Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-03-14T04:18:58.4970267Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-03-14T04:18:58.9808874Z Installing collected packages: ninja
2025-03-14T04:18:58.9892254Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2025-03-14T04:18:58.9892974Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-03-14T04:18:58.9941423Z Successfully installed ninja-1.10.2
2025-03-14T04:18:59.0754957Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:18:59.0756145Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:18:59.0756790Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]]
2025-03-14T04:18:59.0757044Z + install_tlparse
2025-03-14T04:18:59.0757251Z + pip_install --user tlparse==0.3.30
2025-03-14T04:18:59.0757525Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:18:59.0757858Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30
2025-03-14T04:18:59.4019918Z Collecting tlparse==0.3.30
2025-03-14T04:18:59.4146946Z   Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)
2025-03-14T04:18:59.4264348Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
2025-03-14T04:18:59.9318727Z Installing collected packages: tlparse
2025-03-14T04:18:59.9655236Z Successfully installed tlparse-0.3.30
2025-03-14T04:19:00.0631012Z ++ python -m site --user-base
2025-03-14T04:19:00.0869955Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.9/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
2025-03-14T04:19:00.0870780Z + [[ linux-jammy-py3.9-gcc11-build == *asan* ]]
2025-03-14T04:19:00.0871078Z + [[ linux-jammy-py3.9-gcc11-build == *-debug* ]]
2025-03-14T04:19:00.0871365Z + [[ linux-jammy-py3.9-gcc11-build != *-bazel-* ]]
2025-03-14T04:19:00.0871812Z + echo 'We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass'
2025-03-14T04:19:00.0872267Z We are not in debug mode: linux-jammy-py3.9-gcc11-build. Expect the assertion to pass
2025-03-14T04:19:00.0872599Z + cd test
2025-03-14T04:19:00.0872898Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-03-14T04:19:01.2869029Z + [[ cpu_inductor_torchbench == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-03-14T04:19:01.2869468Z + [[ cpu_inductor_torchbench == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-03-14T04:19:01.2869790Z + DYNAMO_BENCHMARK_FLAGS=()
2025-03-14T04:19:01.2870067Z + [[ cpu_inductor_torchbench == *pr_time_benchmarks* ]]
2025-03-14T04:19:01.2870381Z + [[ cpu_inductor_torchbench == *dynamo_eager* ]]
2025-03-14T04:19:01.2870681Z + [[ cpu_inductor_torchbench == *aot_eager* ]]
2025-03-14T04:19:01.2870977Z + [[ cpu_inductor_torchbench == *aot_inductor* ]]
2025-03-14T04:19:01.2871292Z + [[ cpu_inductor_torchbench == *max_autotune_inductor* ]]
2025-03-14T04:19:01.2872084Z + [[ cpu_inductor_torchbench == *inductor* ]]
2025-03-14T04:19:01.2872393Z + [[ cpu_inductor_torchbench != *perf* ]]
2025-03-14T04:19:01.2872719Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor)
2025-03-14T04:19:01.2873019Z + [[ cpu_inductor_torchbench == *dynamic* ]]
2025-03-14T04:19:01.2873276Z + [[ cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:19:01.2873611Z + DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
2025-03-14T04:19:01.2878831Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]]
2025-03-14T04:19:01.2879115Z + [[ linux-jammy-py3.9-gcc11-build == *-bazel-* ]]
2025-03-14T04:19:01.2882521Z + cd test
2025-03-14T04:19:01.2882840Z + python -c 'import torch; print(torch.__config__.show())'
2025-03-14T04:19:02.2400814Z PyTorch built with:
2025-03-14T04:19:02.2402554Z   - GCC 11.4
2025-03-14T04:19:02.2402939Z   - C++ Version: 201703
2025-03-14T04:19:02.2407466Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:19:02.2412018Z   - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:19:02.2416955Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:19:02.2419362Z   - LAPACK is enabled (usually provided by MKL)
2025-03-14T04:19:02.2419692Z   - NNPACK is enabled
2025-03-14T04:19:02.2419913Z   - CPU capability usage: AVX512
2025-03-14T04:19:02.2422617Z   - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=aed0b7a742a2d7b7901790622829cbd2135049a4, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 
2025-03-14T04:19:02.2430020Z 
2025-03-14T04:19:02.4672795Z + cd test
2025-03-14T04:19:02.4674705Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-03-14T04:19:03.4207612Z ATen/Parallel:
2025-03-14T04:19:03.4212101Z 	at::get_num_threads() : 16
2025-03-14T04:19:03.4216541Z 	at::get_num_interop_threads() : 16
2025-03-14T04:19:03.4218605Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-14T04:19:03.4219031Z 	omp_get_max_threads() : 16
2025-03-14T04:19:03.4219570Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-14T04:19:03.4219957Z 	mkl_get_max_threads() : 16
2025-03-14T04:19:03.4220239Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
2025-03-14T04:19:03.4220553Z std::thread::hardware_concurrency() : 32
2025-03-14T04:19:03.4220802Z Environment variables:
2025-03-14T04:19:03.4221001Z 	OMP_NUM_THREADS : [not set]
2025-03-14T04:19:03.4221207Z 	MKL_NUM_THREADS : [not set]
2025-03-14T04:19:03.4221404Z ATen parallel backend: OpenMP
2025-03-14T04:19:03.4221542Z 
2025-03-14T04:19:03.6710479Z + [[ cpu_inductor_torchbench == *numpy_2* ]]
2025-03-14T04:19:03.6711401Z + [[ linux-jammy-py3.9-gcc11-build == *aarch64* ]]
2025-03-14T04:19:03.6711812Z + [[ cpu_inductor_torchbench == *backward* ]]
2025-03-14T04:19:03.6712169Z + [[ cpu_inductor_torchbench == *xla* ]]
2025-03-14T04:19:03.6712422Z + [[ cpu_inductor_torchbench == *executorch* ]]
2025-03-14T04:19:03.6712691Z + [[ cpu_inductor_torchbench == \j\i\t\_\l\e\g\a\c\y ]]
2025-03-14T04:19:03.6713364Z + [[ linux-jammy-py3.9-gcc11-build == *libtorch* ]]
2025-03-14T04:19:03.6713640Z + [[ cpu_inductor_torchbench == distributed ]]
2025-03-14T04:19:03.6713917Z + [[ cpu_inductor_torchbench == *inductor_distributed* ]]
2025-03-14T04:19:03.6714206Z + [[ cpu_inductor_torchbench == *inductor-halide* ]]
2025-03-14T04:19:03.6714610Z + [[ cpu_inductor_torchbench == *inductor-triton-cpu* ]]
2025-03-14T04:19:03.6714913Z + [[ cpu_inductor_torchbench == *inductor-micro-benchmark* ]]
2025-03-14T04:19:03.6715202Z + [[ cpu_inductor_torchbench == *huggingface* ]]
2025-03-14T04:19:03.6715453Z + [[ cpu_inductor_torchbench == *timm* ]]
2025-03-14T04:19:03.6715701Z + [[ cpu_inductor_torchbench == cachebench ]]
2025-03-14T04:19:03.6715964Z + [[ cpu_inductor_torchbench == verify_cachebench ]]
2025-03-14T04:19:03.6716227Z + [[ cpu_inductor_torchbench == *torchbench* ]]
2025-03-14T04:19:03.6716470Z + [[ cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:19:03.6716701Z + install_torchaudio cpu
2025-03-14T04:19:03.6716907Z + local commit
2025-03-14T04:19:03.6789544Z ++ get_pinned_commit audio
2025-03-14T04:19:03.6791876Z ++ cat .github/ci_commit_pins/audio.txt
2025-03-14T04:19:03.6792354Z + commit=c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:03.6795259Z + [[ cpu == \c\u\d\a ]]
2025-03-14T04:19:03.6795928Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:03.6800461Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:19:03.6803310Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:03.9453090Z Collecting git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:03.9453906Z   Cloning https://github.com/pytorch/audio.git (to revision c670ad81fda266b6598aeeef434583eb98197ae8) to /tmp/pip-req-build-nj7mw30n
2025-03-14T04:19:03.9494774Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/audio.git /tmp/pip-req-build-nj7mw30n
2025-03-14T04:19:04.6990102Z   Running command git rev-parse -q --verify 'sha^c670ad81fda266b6598aeeef434583eb98197ae8'
2025-03-14T04:19:04.7024513Z   Running command git fetch -q https://github.com/pytorch/audio.git c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:04.7870095Z   Resolved https://github.com/pytorch/audio.git to commit c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:19:04.7874062Z   Running command git submodule update --init --recursive -q
2025-03-14T04:19:06.9683127Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-14T04:19:06.9717092Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchaudio==2.6.0a0+c670ad8) (2.8.0a0+gitaed0b7a)
2025-03-14T04:19:06.9741177Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.16.1)
2025-03-14T04:19:06.9742199Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (4.12.2)
2025-03-14T04:19:06.9742968Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (1.13.3)
2025-03-14T04:19:06.9743698Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2.8.8)
2025-03-14T04:19:06.9752034Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.1.6)
2025-03-14T04:19:06.9752893Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2024.10.0)
2025-03-14T04:19:06.9793943Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch->torchaudio==2.6.0a0+c670ad8) (1.3.0)
2025-03-14T04:19:07.0216204Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchaudio==2.6.0a0+c670ad8) (3.0.2)
2025-03-14T04:19:07.0266947Z Building wheels for collected packages: torchaudio
2025-03-14T04:20:00.4312631Z   Building wheel for torchaudio (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
2025-03-14T04:20:00.4358048Z [?25h  Created wheel for torchaudio: filename=torchaudio-2.6.0a0+c670ad8-cp39-cp39-linux_x86_64.whl size=1820583 sha256=43b997a372a33aadd5cfd4440c3035216556790253c19207b5605374064eaf45
2025-03-14T04:20:00.4358863Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/1c/6f/84/4c8de1f050144e10889ee8fe7b3a86ac99233001c78656be9d
2025-03-14T04:20:00.4394154Z Successfully built torchaudio
2025-03-14T04:20:00.9137578Z Installing collected packages: torchaudio
2025-03-14T04:20:01.0830292Z Successfully installed torchaudio-2.6.0a0+c670ad8
2025-03-14T04:20:01.2249083Z + install_torchvision
2025-03-14T04:20:01.2253902Z + local orig_preload
2025-03-14T04:20:01.2258107Z + local commit
2025-03-14T04:20:01.2259024Z ++ get_pinned_commit vision
2025-03-14T04:20:01.2259357Z ++ cat .github/ci_commit_pins/vision.txt
2025-03-14T04:20:01.2262456Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:01.2262791Z + orig_preload=
2025-03-14T04:20:01.2262998Z + '[' -n '' ']'
2025-03-14T04:20:01.2263397Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:01.2263855Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:01.2264508Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:01.5280603Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:01.5281636Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-n9010bmx
2025-03-14T04:20:01.5339931Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-n9010bmx
2025-03-14T04:20:03.0299674Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2025-03-14T04:20:03.0327124Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:03.1275783Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:03.4025404Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:20:05.4809871Z   Preparing metadata (setup.py) ... [?25l- \ done
2025-03-14T04:20:05.4846372Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.22.4)
2025-03-14T04:20:05.4847178Z Requirement already satisfied: torch in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.8.0a0+gitaed0b7a)
2025-03-14T04:20:05.4851444Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0)
2025-03-14T04:20:05.4904952Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1)
2025-03-14T04:20:05.4905711Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2)
2025-03-14T04:20:05.4907025Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3)
2025-03-14T04:20:05.4926135Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8)
2025-03-14T04:20:05.4931536Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.6)
2025-03-14T04:20:05.4937805Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0)
2025-03-14T04:20:05.4955647Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from sympy>=1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0)
2025-03-14T04:20:05.5504692Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2)
2025-03-14T04:20:05.5559644Z Building wheels for collected packages: torchvision
2025-03-14T04:20:28.9207972Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
2025-03-14T04:20:28.9226986Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp39-cp39-linux_x86_64.whl size=1218773 sha256=4517ca3af0875f6bf8777bec54bf8dc9189f812698d7e6fdfb9b10f5284e7a21
2025-03-14T04:20:28.9227944Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/76/25/46/00629fe1ec5f276eb28faecc4ae7c48c9ef9e6bbaa0691ad87
2025-03-14T04:20:28.9265434Z Successfully built torchvision
2025-03-14T04:20:29.3880762Z Installing collected packages: torchvision
2025-03-14T04:20:29.7130711Z Successfully installed torchvision-0.19.0a0+d23a6e1
2025-03-14T04:20:29.8274802Z + '[' -n '' ']'
2025-03-14T04:20:29.8276485Z + TORCH_CUDA_ARCH_LIST='8.0;8.6'
2025-03-14T04:20:29.8276990Z + pip_install git+https://github.com/pytorch/ao.git
2025-03-14T04:20:29.8283401Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:29.8285772Z + python3 -m pip install --progress-bar off git+https://github.com/pytorch/ao.git
2025-03-14T04:20:30.0971769Z Collecting git+https://github.com/pytorch/ao.git
2025-03-14T04:20:30.0973874Z   Cloning https://github.com/pytorch/ao.git to /tmp/pip-req-build-5a3ok1fy
2025-03-14T04:20:30.1013529Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-5a3ok1fy
2025-03-14T04:20:30.9671855Z   Resolved https://github.com/pytorch/ao.git to commit 9259584f98db0760b27492a63050a2915c753dbe
2025-03-14T04:20:30.9672334Z   Running command git submodule update --init --recursive -q
2025-03-14T04:20:35.2181259Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-14T04:20:35.2235179Z [?25hBuilding wheels for collected packages: torchao
2025-03-14T04:20:37.4223624Z   Building wheel for torchao (setup.py) ... [?25l- \ | done
2025-03-14T04:20:37.4239688Z [?25h  Created wheel for torchao: filename=torchao-0.10.0+git9259584f-py3-none-any.whl size=687512 sha256=f3c535cbdf8cf155efd92b97f30432305e1246f60b91009b3af0a71368125c26
2025-03-14T04:20:37.4240463Z   Stored in directory: /tmp/pip-ephem-wheel-cache-ovtyv1hb/wheels/7d/5c/37/b607f0d104c0d0de07b506bc734c280ece23dce90e0c5cddc7
2025-03-14T04:20:37.4277404Z Successfully built torchao
2025-03-14T04:20:37.9180637Z Installing collected packages: torchao
2025-03-14T04:20:38.3582198Z Successfully installed torchao-0.10.0+git9259584f
2025-03-14T04:20:38.5539547Z + id=0
2025-03-14T04:20:38.5540155Z + pip_install opencv-python==4.8.0.74
2025-03-14T04:20:38.5540548Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-14T04:20:38.5540897Z + python3 -m pip install --progress-bar off opencv-python==4.8.0.74
2025-03-14T04:20:38.9165974Z Collecting opencv-python==4.8.0.74
2025-03-14T04:20:38.9295358Z   Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)
2025-03-14T04:20:38.9365917Z Requirement already satisfied: numpy>=1.17.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from opencv-python==4.8.0.74) (1.22.4)
2025-03-14T04:20:38.9444648Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)
2025-03-14T04:20:39.8869400Z Installing collected packages: opencv-python
2025-03-14T04:20:40.6683801Z Successfully installed opencv-python-4.8.0.74
2025-03-14T04:20:40.7604058Z + [[ cpu_inductor_torchbench == *inductor_torchbench_smoketest_perf* ]]
2025-03-14T04:20:40.7608218Z + [[ cpu_inductor_torchbench == *inductor_torchbench_cpu_smoketest_perf* ]]
2025-03-14T04:20:40.7611027Z + [[ cpu_inductor_torchbench == *torchbench_gcp_smoketest* ]]
2025-03-14T04:20:40.7611585Z + checkout_install_torchbench
2025-03-14T04:20:40.7616451Z + local commit
2025-03-14T04:20:40.7618760Z ++ get_pinned_commit torchbench
2025-03-14T04:20:40.7619059Z ++ cat .github/ci_commit_pins/torchbench.txt
2025-03-14T04:20:40.7619369Z + commit=373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-14T04:20:40.7619690Z + git clone https://github.com/pytorch/benchmark torchbench
2025-03-14T04:20:40.7630309Z Cloning into 'torchbench'...
2025-03-14T04:20:40.9219209Z remote: Enumerating objects: 35366, done.
2025-03-14T04:20:40.9219676Z remote: Counting objects:   0% (1/5730)
2025-03-14T04:20:40.9219962Z remote: Counting objects:   1% (58/5730)
2025-03-14T04:20:40.9220244Z remote: Counting objects:   2% (115/5730)
2025-03-14T04:20:40.9220520Z remote: Counting objects:   3% (172/5730)
2025-03-14T04:20:40.9220791Z remote: Counting objects:   4% (230/5730)
2025-03-14T04:20:40.9221065Z remote: Counting objects:   5% (287/5730)
2025-03-14T04:20:40.9221452Z remote: Counting objects:   6% (344/5730)
2025-03-14T04:20:40.9221820Z remote: Counting objects:   7% (402/5730)
2025-03-14T04:20:40.9222084Z remote: Counting objects:   8% (459/5730)
2025-03-14T04:20:40.9222347Z remote: Counting objects:   9% (516/5730)
2025-03-14T04:20:40.9222626Z remote: Counting objects:  10% (573/5730)
2025-03-14T04:20:40.9222896Z remote: Counting objects:  11% (631/5730)
2025-03-14T04:20:40.9223156Z remote: Counting objects:  12% (688/5730)
2025-03-14T04:20:40.9223420Z remote: Counting objects:  13% (745/5730)
2025-03-14T04:20:40.9223708Z remote: Counting objects:  14% (803/5730)
2025-03-14T04:20:40.9224128Z remote: Counting objects:  15% (860/5730)
2025-03-14T04:20:40.9224536Z remote: Counting objects:  16% (917/5730)
2025-03-14T04:20:40.9224800Z remote: Counting objects:  17% (975/5730)
2025-03-14T04:20:40.9225083Z remote: Counting objects:  18% (1032/5730)
2025-03-14T04:20:40.9225370Z remote: Counting objects:  19% (1089/5730)
2025-03-14T04:20:40.9225659Z remote: Counting objects:  20% (1146/5730)
2025-03-14T04:20:40.9225945Z remote: Counting objects:  21% (1204/5730)
2025-03-14T04:20:40.9226214Z remote: Counting objects:  22% (1261/5730)
2025-03-14T04:20:40.9226487Z remote: Counting objects:  23% (1318/5730)
2025-03-14T04:20:40.9226755Z remote: Counting objects:  24% (1376/5730)
2025-03-14T04:20:40.9227020Z remote: Counting objects:  25% (1433/5730)
2025-03-14T04:20:40.9227287Z remote: Counting objects:  26% (1490/5730)
2025-03-14T04:20:40.9227556Z remote: Counting objects:  27% (1548/5730)
2025-03-14T04:20:40.9227824Z remote: Counting objects:  28% (1605/5730)
2025-03-14T04:20:40.9228095Z remote: Counting objects:  29% (1662/5730)
2025-03-14T04:20:40.9228363Z remote: Counting objects:  30% (1719/5730)
2025-03-14T04:20:40.9228641Z remote: Counting objects:  31% (1777/5730)
2025-03-14T04:20:40.9228938Z remote: Counting objects:  32% (1834/5730)
2025-03-14T04:20:40.9229320Z remote: Counting objects:  33% (1891/5730)
2025-03-14T04:20:40.9229675Z remote: Counting objects:  34% (1949/5730)
2025-03-14T04:20:40.9229944Z remote: Counting objects:  35% (2006/5730)
2025-03-14T04:20:40.9230207Z remote: Counting objects:  36% (2063/5730)
2025-03-14T04:20:40.9230856Z remote: Counting objects:  37% (2121/5730)
2025-03-14T04:20:40.9231219Z remote: Counting objects:  38% (2178/5730)
2025-03-14T04:20:40.9231598Z remote: Counting objects:  39% (2235/5730)
2025-03-14T04:20:40.9231868Z remote: Counting objects:  40% (2292/5730)
2025-03-14T04:20:40.9232242Z remote: Counting objects:  41% (2350/5730)
2025-03-14T04:20:40.9232512Z remote: Counting objects:  42% (2407/5730)
2025-03-14T04:20:40.9232788Z remote: Counting objects:  43% (2464/5730)
2025-03-14T04:20:40.9233069Z remote: Counting objects:  44% (2522/5730)
2025-03-14T04:20:40.9233343Z remote: Counting objects:  45% (2579/5730)
2025-03-14T04:20:40.9233617Z remote: Counting objects:  46% (2636/5730)
2025-03-14T04:20:40.9233893Z remote: Counting objects:  47% (2694/5730)
2025-03-14T04:20:40.9234167Z remote: Counting objects:  48% (2751/5730)
2025-03-14T04:20:40.9234445Z remote: Counting objects:  49% (2808/5730)
2025-03-14T04:20:40.9234722Z remote: Counting objects:  50% (2865/5730)
2025-03-14T04:20:40.9235001Z remote: Counting objects:  51% (2923/5730)
2025-03-14T04:20:40.9235287Z remote: Counting objects:  52% (2980/5730)
2025-03-14T04:20:40.9235685Z remote: Counting objects:  53% (3037/5730)
2025-03-14T04:20:40.9236038Z remote: Counting objects:  54% (3095/5730)
2025-03-14T04:20:40.9236320Z remote: Counting objects:  55% (3152/5730)
2025-03-14T04:20:40.9236593Z remote: Counting objects:  56% (3209/5730)
2025-03-14T04:20:40.9236869Z remote: Counting objects:  57% (3267/5730)
2025-03-14T04:20:40.9237262Z remote: Counting objects:  58% (3324/5730)
2025-03-14T04:20:40.9237605Z remote: Counting objects:  59% (3381/5730)
2025-03-14T04:20:40.9237883Z remote: Counting objects:  60% (3438/5730)
2025-03-14T04:20:40.9238160Z remote: Counting objects:  61% (3496/5730)
2025-03-14T04:20:40.9238435Z remote: Counting objects:  62% (3553/5730)
2025-03-14T04:20:40.9238711Z remote: Counting objects:  63% (3610/5730)
2025-03-14T04:20:40.9238993Z remote: Counting objects:  64% (3668/5730)
2025-03-14T04:20:40.9239270Z remote: Counting objects:  65% (3725/5730)
2025-03-14T04:20:40.9239547Z remote: Counting objects:  66% (3782/5730)
2025-03-14T04:20:40.9239825Z remote: Counting objects:  67% (3840/5730)
2025-03-14T04:20:40.9240102Z remote: Counting objects:  68% (3897/5730)
2025-03-14T04:20:40.9240388Z remote: Counting objects:  69% (3954/5730)
2025-03-14T04:20:40.9240659Z remote: Counting objects:  70% (4011/5730)
2025-03-14T04:20:40.9240929Z remote: Counting objects:  71% (4069/5730)
2025-03-14T04:20:40.9241198Z remote: Counting objects:  72% (4126/5730)
2025-03-14T04:20:40.9241468Z remote: Counting objects:  73% (4183/5730)
2025-03-14T04:20:40.9241739Z remote: Counting objects:  74% (4241/5730)
2025-03-14T04:20:40.9242010Z remote: Counting objects:  75% (4298/5730)
2025-03-14T04:20:40.9242273Z remote: Counting objects:  76% (4355/5730)
2025-03-14T04:20:40.9242569Z remote: Counting objects:  77% (4413/5730)
2025-03-14T04:20:40.9242836Z remote: Counting objects:  78% (4470/5730)
2025-03-14T04:20:40.9243221Z remote: Counting objects:  79% (4527/5730)
2025-03-14T04:20:40.9243580Z remote: Counting objects:  80% (4584/5730)
2025-03-14T04:20:40.9243848Z remote: Counting objects:  81% (4642/5730)
2025-03-14T04:20:40.9244118Z remote: Counting objects:  82% (4699/5730)
2025-03-14T04:20:40.9244392Z remote: Counting objects:  83% (4756/5730)
2025-03-14T04:20:40.9244654Z remote: Counting objects:  84% (4814/5730)
2025-03-14T04:20:40.9244916Z remote: Counting objects:  85% (4871/5730)
2025-03-14T04:20:40.9245174Z remote: Counting objects:  86% (4928/5730)
2025-03-14T04:20:40.9245431Z remote: Counting objects:  87% (4986/5730)
2025-03-14T04:20:40.9245716Z remote: Counting objects:  88% (5043/5730)
2025-03-14T04:20:40.9245979Z remote: Counting objects:  89% (5100/5730)
2025-03-14T04:20:40.9246242Z remote: Counting objects:  90% (5157/5730)
2025-03-14T04:20:40.9246505Z remote: Counting objects:  91% (5215/5730)
2025-03-14T04:20:40.9246833Z remote: Counting objects:  92% (5272/5730)
2025-03-14T04:20:40.9247101Z remote: Counting objects:  93% (5329/5730)
2025-03-14T04:20:40.9247367Z remote: Counting objects:  94% (5387/5730)
2025-03-14T04:20:40.9247637Z remote: Counting objects:  95% (5444/5730)
2025-03-14T04:20:40.9247965Z remote: Counting objects:  96% (5501/5730)
2025-03-14T04:20:40.9248226Z remote: Counting objects:  97% (5559/5730)
2025-03-14T04:20:40.9248548Z remote: Counting objects:  98% (5616/5730)
2025-03-14T04:20:40.9248939Z remote: Counting objects:  99% (5673/5730)
2025-03-14T04:20:40.9249231Z remote: Counting objects: 100% (5730/5730)
2025-03-14T04:20:40.9249531Z remote: Counting objects: 100% (5730/5730), done.
2025-03-14T04:20:40.9250059Z remote: Compressing objects:   0% (1/629)
2025-03-14T04:20:40.9287324Z remote: Compressing objects:   1% (7/629)
2025-03-14T04:20:40.9307690Z remote: Compressing objects:   2% (13/629)
2025-03-14T04:20:40.9328024Z remote: Compressing objects:   3% (19/629)
2025-03-14T04:20:40.9343319Z remote: Compressing objects:   4% (26/629)
2025-03-14T04:20:40.9367203Z remote: Compressing objects:   5% (32/629)
2025-03-14T04:20:40.9391608Z remote: Compressing objects:   6% (38/629)
2025-03-14T04:20:40.9410385Z remote: Compressing objects:   7% (45/629)
2025-03-14T04:20:40.9415223Z remote: Compressing objects:   8% (51/629)
2025-03-14T04:20:40.9419433Z remote: Compressing objects:   9% (57/629)
2025-03-14T04:20:40.9504045Z remote: Compressing objects:  10% (63/629)
2025-03-14T04:20:40.9551358Z remote: Compressing objects:  11% (70/629)
2025-03-14T04:20:40.9624878Z remote: Compressing objects:  12% (76/629)
2025-03-14T04:20:40.9724094Z remote: Compressing objects:  13% (82/629)
2025-03-14T04:20:40.9784606Z remote: Compressing objects:  14% (89/629)
2025-03-14T04:20:40.9802018Z remote: Compressing objects:  15% (95/629)
2025-03-14T04:20:40.9857050Z remote: Compressing objects:  16% (101/629)
2025-03-14T04:20:40.9904065Z remote: Compressing objects:  17% (107/629)
2025-03-14T04:20:40.9944706Z remote: Compressing objects:  18% (114/629)
2025-03-14T04:20:40.9951397Z remote: Compressing objects:  19% (120/629)
2025-03-14T04:20:40.9995481Z remote: Compressing objects:  20% (126/629)
2025-03-14T04:20:41.0028519Z remote: Compressing objects:  21% (133/629)
2025-03-14T04:20:41.0060144Z remote: Compressing objects:  22% (139/629)
2025-03-14T04:20:41.0082251Z remote: Compressing objects:  23% (145/629)
2025-03-14T04:20:41.0111464Z remote: Compressing objects:  24% (151/629)
2025-03-14T04:20:41.0129634Z remote: Compressing objects:  25% (158/629)
2025-03-14T04:20:41.0149989Z remote: Compressing objects:  26% (164/629)
2025-03-14T04:20:41.0164664Z remote: Compressing objects:  27% (170/629)
2025-03-14T04:20:41.0183941Z remote: Compressing objects:  28% (177/629)
2025-03-14T04:20:41.0201269Z remote: Compressing objects:  29% (183/629)
2025-03-14T04:20:41.0216094Z remote: Compressing objects:  30% (189/629)
2025-03-14T04:20:41.0229756Z remote: Compressing objects:  31% (195/629)
2025-03-14T04:20:41.0245472Z remote: Compressing objects:  32% (202/629)
2025-03-14T04:20:41.0255033Z remote: Compressing objects:  33% (208/629)
2025-03-14T04:20:41.0265107Z remote: Compressing objects:  34% (214/629)
2025-03-14T04:20:41.0279111Z remote: Compressing objects:  35% (221/629)
2025-03-14T04:20:41.0289140Z remote: Compressing objects:  36% (227/629)
2025-03-14T04:20:41.0290196Z remote: Compressing objects:  37% (233/629)
2025-03-14T04:20:41.0297012Z remote: Compressing objects:  38% (240/629)
2025-03-14T04:20:41.0299176Z remote: Compressing objects:  39% (246/629)
2025-03-14T04:20:41.0299653Z remote: Compressing objects:  40% (252/629)
2025-03-14T04:20:41.0300026Z remote: Compressing objects:  41% (258/629)
2025-03-14T04:20:41.0300442Z remote: Compressing objects:  42% (265/629)
2025-03-14T04:20:41.0300813Z remote: Compressing objects:  43% (271/629)
2025-03-14T04:20:41.0301975Z remote: Compressing objects:  44% (277/629)
2025-03-14T04:20:41.0302268Z remote: Compressing objects:  45% (284/629)
2025-03-14T04:20:41.0318760Z remote: Compressing objects:  46% (290/629)
2025-03-14T04:20:41.0322561Z remote: Compressing objects:  47% (296/629)
2025-03-14T04:20:41.0323176Z remote: Compressing objects:  48% (302/629)
2025-03-14T04:20:41.0323465Z remote: Compressing objects:  49% (309/629)
2025-03-14T04:20:41.0323747Z remote: Compressing objects:  50% (315/629)
2025-03-14T04:20:41.0329749Z remote: Compressing objects:  51% (321/629)
2025-03-14T04:20:41.0330304Z remote: Compressing objects:  52% (328/629)
2025-03-14T04:20:41.0330726Z remote: Compressing objects:  53% (334/629)
2025-03-14T04:20:41.0334705Z remote: Compressing objects:  54% (340/629)
2025-03-14T04:20:41.0339942Z remote: Compressing objects:  55% (346/629)
2025-03-14T04:20:41.0340309Z remote: Compressing objects:  56% (353/629)
2025-03-14T04:20:41.0340633Z remote: Compressing objects:  57% (359/629)
2025-03-14T04:20:41.0362764Z remote: Compressing objects:  58% (365/629)
2025-03-14T04:20:41.0367051Z remote: Compressing objects:  59% (372/629)
2025-03-14T04:20:41.0371862Z remote: Compressing objects:  60% (378/629)
2025-03-14T04:20:41.0377710Z remote: Compressing objects:  61% (384/629)
2025-03-14T04:20:41.0379402Z remote: Compressing objects:  62% (390/629)
2025-03-14T04:20:41.0379709Z remote: Compressing objects:  63% (397/629)
2025-03-14T04:20:41.0379999Z remote: Compressing objects:  64% (403/629)
2025-03-14T04:20:41.0380288Z remote: Compressing objects:  65% (409/629)
2025-03-14T04:20:41.0380583Z remote: Compressing objects:  66% (416/629)
2025-03-14T04:20:41.0380861Z remote: Compressing objects:  67% (422/629)
2025-03-14T04:20:41.0381135Z remote: Compressing objects:  68% (428/629)
2025-03-14T04:20:41.0381589Z remote: Compressing objects:  69% (435/629)
2025-03-14T04:20:41.0381894Z remote: Compressing objects:  70% (441/629)
2025-03-14T04:20:41.0382188Z remote: Compressing objects:  71% (447/629)
2025-03-14T04:20:41.0382471Z remote: Compressing objects:  72% (453/629)
2025-03-14T04:20:41.0382752Z remote: Compressing objects:  73% (460/629)
2025-03-14T04:20:41.0383023Z remote: Compressing objects:  74% (466/629)
2025-03-14T04:20:41.0383307Z remote: Compressing objects:  75% (472/629)
2025-03-14T04:20:41.0428020Z remote: Compressing objects:  76% (479/629)
2025-03-14T04:20:41.0432483Z remote: Compressing objects:  77% (485/629)
2025-03-14T04:20:41.0436681Z remote: Compressing objects:  78% (491/629)
2025-03-14T04:20:41.0441461Z remote: Compressing objects:  79% (497/629)
2025-03-14T04:20:41.0446080Z remote: Compressing objects:  80% (504/629)
2025-03-14T04:20:41.0450221Z remote: Compressing objects:  81% (510/629)
2025-03-14T04:20:41.0450726Z remote: Compressing objects:  82% (516/629)
2025-03-14T04:20:41.0451083Z remote: Compressing objects:  83% (523/629)
2025-03-14T04:20:41.0455530Z remote: Compressing objects:  84% (529/629)
2025-03-14T04:20:41.0457950Z remote: Compressing objects:  85% (535/629)
2025-03-14T04:20:41.0458319Z remote: Compressing objects:  86% (541/629)
2025-03-14T04:20:41.0458601Z remote: Compressing objects:  87% (548/629)
2025-03-14T04:20:41.0458909Z remote: Compressing objects:  88% (554/629)
2025-03-14T04:20:41.0459188Z remote: Compressing objects:  89% (560/629)
2025-03-14T04:20:41.0459471Z remote: Compressing objects:  90% (567/629)
2025-03-14T04:20:41.0459748Z remote: Compressing objects:  91% (573/629)
2025-03-14T04:20:41.0460023Z remote: Compressing objects:  92% (579/629)
2025-03-14T04:20:41.0460298Z remote: Compressing objects:  93% (585/629)
2025-03-14T04:20:41.0460575Z remote: Compressing objects:  94% (592/629)
2025-03-14T04:20:41.0460855Z remote: Compressing objects:  95% (598/629)
2025-03-14T04:20:41.0461133Z remote: Compressing objects:  96% (604/629)
2025-03-14T04:20:41.0461411Z remote: Compressing objects:  97% (611/629)
2025-03-14T04:20:41.0462538Z remote: Compressing objects:  98% (617/629)
2025-03-14T04:20:41.0462842Z remote: Compressing objects:  99% (623/629)
2025-03-14T04:20:41.0463120Z remote: Compressing objects: 100% (629/629)
2025-03-14T04:20:41.0463423Z remote: Compressing objects: 100% (629/629), done.
2025-03-14T04:20:41.0532577Z Receiving objects:   0% (1/35366)
2025-03-14T04:20:41.0567615Z Receiving objects:   1% (354/35366)
2025-03-14T04:20:41.0605938Z Receiving objects:   2% (708/35366)
2025-03-14T04:20:41.0635286Z Receiving objects:   3% (1061/35366)
2025-03-14T04:20:41.0664734Z Receiving objects:   4% (1415/35366)
2025-03-14T04:20:41.0693525Z Receiving objects:   5% (1769/35366)
2025-03-14T04:20:41.0730952Z Receiving objects:   6% (2122/35366)
2025-03-14T04:20:41.0763132Z Receiving objects:   7% (2476/35366)
2025-03-14T04:20:41.0788531Z Receiving objects:   8% (2830/35366)
2025-03-14T04:20:41.0846454Z Receiving objects:   9% (3183/35366)
2025-03-14T04:20:41.0898533Z Receiving objects:  10% (3537/35366)
2025-03-14T04:20:41.1046842Z Receiving objects:  11% (3891/35366)
2025-03-14T04:20:41.1207308Z Receiving objects:  12% (4244/35366)
2025-03-14T04:20:41.1341715Z Receiving objects:  13% (4598/35366)
2025-03-14T04:20:41.1428545Z Receiving objects:  14% (4952/35366)
2025-03-14T04:20:41.1484740Z Receiving objects:  15% (5305/35366)
2025-03-14T04:20:41.1509961Z Receiving objects:  16% (5659/35366)
2025-03-14T04:20:41.1538998Z Receiving objects:  17% (6013/35366)
2025-03-14T04:20:41.1617217Z Receiving objects:  18% (6366/35366)
2025-03-14T04:20:41.1632486Z Receiving objects:  19% (6720/35366)
2025-03-14T04:20:41.2934005Z Receiving objects:  20% (7074/35366)
2025-03-14T04:20:42.0425582Z Receiving objects:  21% (7427/35366)
2025-03-14T04:20:42.4402434Z Receiving objects:  21% (7681/35366), 42.08 MiB | 84.15 MiB/s
2025-03-14T04:20:42.5286121Z Receiving objects:  22% (7781/35366), 89.86 MiB | 89.85 MiB/s
2025-03-14T04:20:42.5959372Z Receiving objects:  23% (8135/35366), 89.86 MiB | 89.85 MiB/s
2025-03-14T04:20:42.6847250Z Receiving objects:  24% (8488/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:42.7643585Z Receiving objects:  25% (8842/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:42.8435238Z Receiving objects:  26% (9196/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:42.9091537Z Receiving objects:  27% (9549/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:42.9739106Z Receiving objects:  28% (9903/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:43.0432546Z Receiving objects:  29% (10257/35366), 129.60 MiB | 86.39 MiB/s
2025-03-14T04:20:43.1596618Z Receiving objects:  29% (10362/35366), 173.77 MiB | 86.88 MiB/s
2025-03-14T04:20:43.2074879Z Receiving objects:  30% (10610/35366), 173.77 MiB | 86.88 MiB/s
2025-03-14T04:20:43.4150449Z Receiving objects:  31% (10964/35366), 173.77 MiB | 86.88 MiB/s
2025-03-14T04:20:43.6169523Z Receiving objects:  32% (11318/35366), 173.77 MiB | 86.88 MiB/s
2025-03-14T04:20:43.6179633Z Receiving objects:  33% (11671/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6192259Z Receiving objects:  34% (12025/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6208174Z Receiving objects:  35% (12379/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6214299Z Receiving objects:  36% (12732/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6227079Z Receiving objects:  37% (13086/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6242040Z Receiving objects:  38% (13440/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6266736Z Receiving objects:  39% (13793/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6267213Z Receiving objects:  40% (14147/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6278157Z Receiving objects:  41% (14501/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6447911Z Receiving objects:  42% (14854/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6452611Z Receiving objects:  43% (15208/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6462870Z Receiving objects:  44% (15562/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6479370Z Receiving objects:  45% (15915/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6486098Z Receiving objects:  46% (16269/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6495680Z Receiving objects:  47% (16623/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6610377Z Receiving objects:  48% (16976/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6611086Z Receiving objects:  49% (17330/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6621387Z Receiving objects:  50% (17683/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6623439Z Receiving objects:  51% (18037/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.6856135Z Receiving objects:  52% (18391/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7179806Z Receiving objects:  53% (18744/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7187417Z Receiving objects:  54% (19098/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7200201Z Receiving objects:  55% (19452/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7208937Z Receiving objects:  56% (19805/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7226059Z Receiving objects:  57% (20159/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7230998Z Receiving objects:  58% (20513/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7241145Z Receiving objects:  59% (20866/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7415099Z Receiving objects:  60% (21220/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7441567Z Receiving objects:  61% (21574/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7449406Z Receiving objects:  62% (21927/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7458335Z Receiving objects:  63% (22281/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7499720Z Receiving objects:  64% (22635/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7607932Z Receiving objects:  65% (22988/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7608507Z Receiving objects:  66% (23342/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7620370Z Receiving objects:  67% (23696/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7620768Z Receiving objects:  68% (24049/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7628451Z Receiving objects:  69% (24403/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7631553Z Receiving objects:  70% (24757/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7736521Z Receiving objects:  71% (25110/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7745234Z Receiving objects:  72% (25464/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7760750Z Receiving objects:  73% (25818/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7784252Z Receiving objects:  74% (26171/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7803719Z Receiving objects:  75% (26525/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7822304Z Receiving objects:  76% (26879/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7836826Z Receiving objects:  77% (27232/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7849862Z Receiving objects:  78% (27586/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7890935Z Receiving objects:  79% (27940/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7946793Z Receiving objects:  80% (28293/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7960335Z Receiving objects:  81% (28647/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.7970805Z Receiving objects:  82% (29001/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8066671Z Receiving objects:  83% (29354/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8069178Z Receiving objects:  84% (29708/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8071577Z Receiving objects:  85% (30062/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8072602Z Receiving objects:  86% (30415/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8074907Z Receiving objects:  87% (30769/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8077989Z Receiving objects:  88% (31123/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8083259Z Receiving objects:  89% (31476/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8083638Z Receiving objects:  90% (31830/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8087926Z Receiving objects:  91% (32184/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8092033Z Receiving objects:  92% (32537/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8100893Z Receiving objects:  93% (32891/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8101253Z Receiving objects:  94% (33245/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8119642Z Receiving objects:  95% (33598/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8178102Z Receiving objects:  96% (33952/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8185815Z Receiving objects:  97% (34306/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8221905Z Receiving objects:  98% (34659/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8255088Z Receiving objects:  99% (35013/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8258885Z remote: Total 35366 (delta 5421), reused 5108 (delta 5101), pack-reused 29636 (from 2)
2025-03-14T04:20:43.8265500Z Receiving objects: 100% (35366/35366), 213.79 MiB | 85.51 MiB/s
2025-03-14T04:20:43.8265906Z Receiving objects: 100% (35366/35366), 233.83 MiB | 83.99 MiB/s, done.
2025-03-14T04:20:43.8312001Z Resolving deltas:   0% (0/19977)
2025-03-14T04:20:43.8335770Z Resolving deltas:   1% (200/19977)
2025-03-14T04:20:43.8349397Z Resolving deltas:   2% (413/19977)
2025-03-14T04:20:43.8421694Z Resolving deltas:   3% (600/19977)
2025-03-14T04:20:43.8472192Z Resolving deltas:   4% (800/19977)
2025-03-14T04:20:43.8495415Z Resolving deltas:   5% (999/19977)
2025-03-14T04:20:43.8495716Z Resolving deltas:   6% (1199/19977)
2025-03-14T04:20:43.8537680Z Resolving deltas:   7% (1399/19977)
2025-03-14T04:20:43.8538112Z Resolving deltas:   8% (1599/19977)
2025-03-14T04:20:43.8538436Z Resolving deltas:   9% (1798/19977)
2025-03-14T04:20:43.8538711Z Resolving deltas:  10% (1998/19977)
2025-03-14T04:20:43.8539049Z Resolving deltas:  11% (2198/19977)
2025-03-14T04:20:43.8539346Z Resolving deltas:  12% (2398/19977)
2025-03-14T04:20:43.8539658Z Resolving deltas:  13% (2598/19977)
2025-03-14T04:20:43.8546411Z Resolving deltas:  14% (2797/19977)
2025-03-14T04:20:43.8554079Z Resolving deltas:  15% (2997/19977)
2025-03-14T04:20:43.8564622Z Resolving deltas:  16% (3197/19977)
2025-03-14T04:20:43.8567797Z Resolving deltas:  17% (3397/19977)
2025-03-14T04:20:43.8583424Z Resolving deltas:  18% (3596/19977)
2025-03-14T04:20:43.8583771Z Resolving deltas:  19% (3796/19977)
2025-03-14T04:20:43.8592937Z Resolving deltas:  20% (3996/19977)
2025-03-14T04:20:43.8606444Z Resolving deltas:  21% (4196/19977)
2025-03-14T04:20:43.8621078Z Resolving deltas:  22% (4395/19977)
2025-03-14T04:20:43.8625574Z Resolving deltas:  23% (4595/19977)
2025-03-14T04:20:43.8633405Z Resolving deltas:  24% (4795/19977)
2025-03-14T04:20:43.8642218Z Resolving deltas:  25% (4995/19977)
2025-03-14T04:20:43.8645926Z Resolving deltas:  26% (5195/19977)
2025-03-14T04:20:43.8656073Z Resolving deltas:  27% (5394/19977)
2025-03-14T04:20:43.8666629Z Resolving deltas:  28% (5594/19977)
2025-03-14T04:20:43.8673254Z Resolving deltas:  29% (5794/19977)
2025-03-14T04:20:43.8682954Z Resolving deltas:  30% (5994/19977)
2025-03-14T04:20:43.8683332Z Resolving deltas:  31% (6193/19977)
2025-03-14T04:20:43.8686692Z Resolving deltas:  32% (6394/19977)
2025-03-14T04:20:43.8692999Z Resolving deltas:  33% (6593/19977)
2025-03-14T04:20:43.8697952Z Resolving deltas:  34% (6793/19977)
2025-03-14T04:20:43.8715253Z Resolving deltas:  35% (6992/19977)
2025-03-14T04:20:43.8731859Z Resolving deltas:  36% (7192/19977)
2025-03-14T04:20:43.8733045Z Resolving deltas:  37% (7392/19977)
2025-03-14T04:20:43.8733294Z Resolving deltas:  38% (7592/19977)
2025-03-14T04:20:43.8753039Z Resolving deltas:  39% (7792/19977)
2025-03-14T04:20:43.8773749Z Resolving deltas:  40% (7991/19977)
2025-03-14T04:20:43.8779336Z Resolving deltas:  41% (8191/19977)
2025-03-14T04:20:43.8797556Z Resolving deltas:  42% (8391/19977)
2025-03-14T04:20:43.8798265Z Resolving deltas:  43% (8591/19977)
2025-03-14T04:20:43.8803021Z Resolving deltas:  44% (8790/19977)
2025-03-14T04:20:43.8810998Z Resolving deltas:  45% (8990/19977)
2025-03-14T04:20:43.8815723Z Resolving deltas:  46% (9190/19977)
2025-03-14T04:20:43.8824304Z Resolving deltas:  47% (9390/19977)
2025-03-14T04:20:43.8830117Z Resolving deltas:  48% (9589/19977)
2025-03-14T04:20:43.8835607Z Resolving deltas:  49% (9789/19977)
2025-03-14T04:20:43.8844581Z Resolving deltas:  50% (9990/19977)
2025-03-14T04:20:43.8849925Z Resolving deltas:  51% (10189/19977)
2025-03-14T04:20:43.8857686Z Resolving deltas:  52% (10389/19977)
2025-03-14T04:20:43.8871205Z Resolving deltas:  53% (10588/19977)
2025-03-14T04:20:43.8877774Z Resolving deltas:  54% (10788/19977)
2025-03-14T04:20:43.8909016Z Resolving deltas:  55% (10988/19977)
2025-03-14T04:20:43.8920148Z Resolving deltas:  56% (11188/19977)
2025-03-14T04:20:43.8927815Z Resolving deltas:  57% (11387/19977)
2025-03-14T04:20:43.8936163Z Resolving deltas:  58% (11587/19977)
2025-03-14T04:20:43.8945968Z Resolving deltas:  59% (11787/19977)
2025-03-14T04:20:43.8954470Z Resolving deltas:  60% (11987/19977)
2025-03-14T04:20:43.8962819Z Resolving deltas:  61% (12186/19977)
2025-03-14T04:20:43.8967801Z Resolving deltas:  62% (12386/19977)
2025-03-14T04:20:43.8976393Z Resolving deltas:  63% (12586/19977)
2025-03-14T04:20:43.8980564Z Resolving deltas:  64% (12786/19977)
2025-03-14T04:20:43.8994336Z Resolving deltas:  65% (12986/19977)
2025-03-14T04:20:43.8997651Z Resolving deltas:  66% (13185/19977)
2025-03-14T04:20:43.9003490Z Resolving deltas:  67% (13385/19977)
2025-03-14T04:20:43.9008834Z Resolving deltas:  68% (13585/19977)
2025-03-14T04:20:43.9017176Z Resolving deltas:  69% (13785/19977)
2025-03-14T04:20:43.9019441Z Resolving deltas:  70% (13984/19977)
2025-03-14T04:20:43.9031646Z Resolving deltas:  71% (14184/19977)
2025-03-14T04:20:43.9041457Z Resolving deltas:  72% (14384/19977)
2025-03-14T04:20:43.9065084Z Resolving deltas:  73% (14584/19977)
2025-03-14T04:20:43.9071567Z Resolving deltas:  74% (14783/19977)
2025-03-14T04:20:43.9085968Z Resolving deltas:  75% (14983/19977)
2025-03-14T04:20:43.9099064Z Resolving deltas:  76% (15183/19977)
2025-03-14T04:20:43.9102798Z Resolving deltas:  77% (15383/19977)
2025-03-14T04:20:43.9108856Z Resolving deltas:  78% (15583/19977)
2025-03-14T04:20:43.9124798Z Resolving deltas:  79% (15782/19977)
2025-03-14T04:20:43.9133052Z Resolving deltas:  80% (15982/19977)
2025-03-14T04:20:43.9144838Z Resolving deltas:  81% (16182/19977)
2025-03-14T04:20:43.9153717Z Resolving deltas:  82% (16382/19977)
2025-03-14T04:20:43.9167486Z Resolving deltas:  83% (16581/19977)
2025-03-14T04:20:43.9179639Z Resolving deltas:  84% (16781/19977)
2025-03-14T04:20:43.9183772Z Resolving deltas:  85% (16981/19977)
2025-03-14T04:20:43.9194381Z Resolving deltas:  86% (17181/19977)
2025-03-14T04:20:43.9217645Z Resolving deltas:  87% (17380/19977)
2025-03-14T04:20:43.9222055Z Resolving deltas:  88% (17580/19977)
2025-03-14T04:20:43.9238460Z Resolving deltas:  89% (17780/19977)
2025-03-14T04:20:43.9246608Z Resolving deltas:  90% (17980/19977)
2025-03-14T04:20:43.9255237Z Resolving deltas:  91% (18180/19977)
2025-03-14T04:20:43.9263868Z Resolving deltas:  92% (18379/19977)
2025-03-14T04:20:43.9269097Z Resolving deltas:  93% (18579/19977)
2025-03-14T04:20:43.9299056Z Resolving deltas:  94% (18779/19977)
2025-03-14T04:20:43.9302784Z Resolving deltas:  95% (18979/19977)
2025-03-14T04:20:43.9308533Z Resolving deltas:  96% (19178/19977)
2025-03-14T04:20:43.9308882Z Resolving deltas:  97% (19378/19977)
2025-03-14T04:20:43.9342631Z Resolving deltas:  98% (19578/19977)
2025-03-14T04:20:43.9389085Z Resolving deltas:  99% (19778/19977)
2025-03-14T04:20:43.9391615Z Resolving deltas: 100% (19977/19977)
2025-03-14T04:20:43.9391881Z Resolving deltas: 100% (19977/19977), done.
2025-03-14T04:20:44.8890205Z + pushd torchbench
2025-03-14T04:20:44.8890489Z ~/workspace/torchbench ~/workspace
2025-03-14T04:20:44.8890776Z + git checkout 373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-14T04:20:44.9162745Z Note: switching to '373ffb19dc470f4423a3176a4133f8f4b3cdb5bd'.
2025-03-14T04:20:44.9162997Z 
2025-03-14T04:20:44.9163163Z You are in 'detached HEAD' state. You can look around, make experimental
2025-03-14T04:20:44.9163510Z changes and commit them, and you can discard any commits you make in this
2025-03-14T04:20:44.9164032Z state without impacting any branches by switching back to a branch.
2025-03-14T04:20:44.9164224Z 
2025-03-14T04:20:44.9164368Z If you want to create a new branch to retain commits you create, you may
2025-03-14T04:20:44.9164690Z do so (now or later) by using -c with the switch command. Example:
2025-03-14T04:20:44.9164866Z 
2025-03-14T04:20:44.9164966Z   git switch -c <new-branch-name>
2025-03-14T04:20:44.9165100Z 
2025-03-14T04:20:44.9165194Z Or undo this operation with:
2025-03-14T04:20:44.9165318Z 
2025-03-14T04:20:44.9165399Z   git switch -
2025-03-14T04:20:44.9165499Z 
2025-03-14T04:20:44.9165663Z Turn off this advice by setting config variable advice.detachedHead to false
2025-03-14T04:20:44.9165872Z 
2025-03-14T04:20:44.9166021Z HEAD is now at 373ffb19 Copy model before benchmark warmup runs (#145858)
2025-03-14T04:20:44.9166309Z + '[' '' ']'
2025-03-14T04:20:44.9166507Z + python install.py --continue_on_fail
2025-03-14T04:20:49.8862151Z checking packages numpy, torch, torchvision, torchaudio are installed, generating constaints...OK
2025-03-14T04:21:14.1890032Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch...OK
2025-03-14T04:21:25.1949523Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Background_Matting...OK
2025-03-14T04:21:37.0887762Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/LearningToPaint...OK
2025-03-14T04:21:48.5337984Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Super_SloMo...OK
2025-03-14T04:21:58.8400756Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/alexnet...OK
2025-03-14T04:22:15.2348540Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_edgecnn...OK
2025-03-14T04:22:27.9262121Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gcn...OK
2025-03-14T04:22:40.6294855Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gin...OK
2025-03-14T04:22:53.2304690Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_sage...OK
2025-03-14T04:22:53.2305845Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/cm3leon_generate...SKIP - No install.py is found
2025-03-14T04:23:04.6017491Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dcgan...OK
2025-03-14T04:23:17.1940364Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/demucs...OK
2025-03-14T04:23:28.0991300Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/densenet121...OK
2025-03-14T04:24:10.1893738Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_c4...OK
2025-03-14T04:24:29.7272986Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_dc5...OK
2025-03-14T04:24:47.3410465Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_fpn...OK
2025-03-14T04:25:05.7732945Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_c4...OK
2025-03-14T04:25:26.3572163Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_dc5...OK
2025-03-14T04:25:43.8506300Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_fpn...OK
2025-03-14T04:26:00.6943025Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fcos_r_50_fpn...OK
2025-03-14T04:26:18.2806154Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn...OK
2025-03-14T04:26:35.4540914Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_c4...OK
2025-03-14T04:26:52.5855831Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_fpn...OK
2025-03-14T04:27:10.2489521Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_c4...OK
2025-03-14T04:27:27.9339699Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_fpn...OK
2025-03-14T04:27:39.0856466Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm...OK
2025-03-14T04:28:06.3084312Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_det_predictor...OK
2025-03-14T04:28:23.0261371Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_reco_predictor...OK
2025-03-14T04:28:37.8216330Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/drq...OK
2025-03-14T04:28:56.1980979Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/fastNLP_Bert...OK
2025-03-14T04:29:07.8977964Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_dp_cifar10...OK
2025-03-14T04:29:19.5367464Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_maml_omniglot...OK
2025-03-14T04:29:36.5786794Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Albert...OK
2025-03-14T04:29:53.5192031Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bart...OK
2025-03-14T04:30:08.9796229Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert...OK
2025-03-14T04:30:25.5916805Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert_large...OK
2025-03-14T04:30:41.2523108Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_BigBird...OK
2025-03-14T04:30:57.2265448Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_DistilBert...OK
2025-03-14T04:31:13.6798768Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2...OK
2025-03-14T04:31:36.0966367Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2_large...OK
2025-03-14T04:31:52.0080652Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Longformer...OK
2025-03-14T04:32:07.2323918Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Reformer...OK
2025-03-14T04:32:26.3012067Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Roberta_base...OK
2025-03-14T04:32:42.1878514Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5...OK
2025-03-14T04:32:59.0643714Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_base...OK
2025-03-14T04:32:59.0644595Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_generate...SKIP - No install.py is found
2025-03-14T04:33:21.0206427Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_large...OK
2025-03-14T04:33:35.3360121Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Whisper...OK
2025-03-14T04:33:35.3362862Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_clip...SKIP - No install.py is found
2025-03-14T04:33:53.4575005Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper...OK
2025-03-14T04:34:05.3051740Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/lennard_jones...OK
2025-03-14T04:34:17.5445351Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama...OK
2025-03-14T04:35:00.1288532Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama_v2_7b_16h...OK
2025-03-14T04:36:10.6949942Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llava...OK
2025-03-14T04:36:21.7732437Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml...OK
2025-03-14T04:36:33.9818639Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml_omniglot...OK
2025-03-14T04:36:33.9821411Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/microbench_unbacked_tolist_sum...SKIP - No install.py is found
2025-03-14T04:36:44.9150555Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mnasnet1_0...OK
2025-03-14T04:36:55.6221283Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2...OK
2025-03-14T04:37:06.3309020Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2_quantized_qat...OK
2025-03-14T04:37:17.5180358Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v3_large...OK
2025-03-14T04:37:28.3466954Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moco...OK
2025-03-14T04:37:55.0932625Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moondream...OK
2025-03-14T04:37:55.0935832Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nanogpt...SKIP - No install.py is found
2025-03-14T04:38:07.1300836Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nvidia_deeprecommender...OK
2025-03-14T04:38:20.1725898Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/opacus_cifar10...OK
2025-03-14T04:38:31.0744822Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_densenet...OK
2025-03-14T04:38:41.8733807Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_resnet...OK
2025-03-14T04:38:52.6477113Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_equation_of_state...OK
2025-03-14T04:39:03.4286163Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_isoneutral_mixing...OK
2025-03-14T04:39:14.1469570Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_turbulent_kinetic_energy...OK
2025-03-14T04:39:30.0247585Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix...OK
2025-03-14T04:39:42.7423386Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_stargan...OK
2025-03-14T04:39:56.6452413Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_unet...OK
2025-03-14T04:40:07.6239328Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet152...OK
2025-03-14T04:40:19.2955956Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet18...OK
2025-03-14T04:40:31.0306820Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50...OK
2025-03-14T04:40:41.9647097Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50_quantized_qat...OK
2025-03-14T04:40:53.0192220Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnext50_32x4d...OK
2025-03-14T04:41:12.6739241Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam...OK
2025-03-14T04:41:33.2086861Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam_fast...OK
2025-03-14T04:41:44.3473733Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/shufflenet_v2_x1_0...OK
2025-03-14T04:41:44.3479868Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt...SKIP - No install.py is found
2025-03-14T04:41:44.3483321Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt_tp_manual...SKIP - No install.py is found
2025-03-14T04:41:58.0289162Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/soft_actor_critic...OK
2025-03-14T04:42:10.4577190Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/speech_transformer...OK
2025-03-14T04:42:20.5141657Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/squeezenet1_1...OK
2025-03-14T04:42:53.6989323Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_text_encoder...OK
2025-03-14T04:43:09.2854279Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_unet...OK
2025-03-14T04:43:25.3625531Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tacotron2...OK
2025-03-14T04:43:43.2780408Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientdet...OK
2025-03-14T04:43:53.8063950Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientnet...OK
2025-03-14T04:44:04.6199829Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_nfnet...OK
2025-03-14T04:44:15.1090040Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_regnet...OK
2025-03-14T04:44:25.4587387Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_resnest...OK
2025-03-14T04:44:35.9992185Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer...OK
2025-03-14T04:44:47.0587002Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer_large...OK
2025-03-14T04:44:57.7767030Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vovnet...OK
2025-03-14T04:45:13.8678166Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/torch_multimodal_clip...OK
2025-03-14T04:45:29.1531278Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tts_angular...OK
2025-03-14T04:45:40.0415042Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vgg16...OK
2025-03-14T04:45:51.9141057Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vision_maskrcnn...OK
2025-03-14T04:46:04.4805912Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/yolov3...OK
2025-03-14T04:46:10.1497374Z installed torchbench with package constraints: {'numpy': '1.22.4', 'torch': '2.8.0a0+gitaed0b7a', 'torchvision': '0.19.0a0+d23a6e1', 'torchaudio': '2.6.0a0+c670ad8'}
2025-03-14T04:46:10.4816089Z + pip install transformers==4.38.1
2025-03-14T04:46:10.8319084Z Collecting transformers==4.38.1
2025-03-14T04:46:10.8432911Z   Downloading transformers-4.38.1-py3-none-any.whl.metadata (131 kB)
2025-03-14T04:46:10.9838411Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (3.16.1)
2025-03-14T04:46:10.9840426Z Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.29.3)
2025-03-14T04:46:10.9846640Z Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (1.22.4)
2025-03-14T04:46:10.9850880Z Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (24.2)
2025-03-14T04:46:10.9855143Z Requirement already satisfied: pyyaml>=5.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (6.0.2)
2025-03-14T04:46:10.9855933Z Requirement already satisfied: regex!=2019.12.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2024.11.6)
2025-03-14T04:46:10.9857915Z Requirement already satisfied: requests in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (2.32.3)
2025-03-14T04:46:11.0789920Z Collecting tokenizers<0.19,>=0.14 (from transformers==4.38.1)
2025-03-14T04:46:11.0803159Z   Using cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
2025-03-14T04:46:11.0823472Z Requirement already satisfied: safetensors>=0.4.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (0.5.3)
2025-03-14T04:46:11.0824811Z Requirement already satisfied: tqdm>=4.27 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from transformers==4.38.1) (4.67.1)
2025-03-14T04:46:11.0980883Z Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (2024.10.0)
2025-03-14T04:46:11.0982115Z Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (4.12.2)
2025-03-14T04:46:11.1114988Z Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.4.1)
2025-03-14T04:46:11.1115863Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (3.10)
2025-03-14T04:46:11.1120517Z Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (1.26.20)
2025-03-14T04:46:11.1121359Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/py_3.9/lib/python3.9/site-packages (from requests->transformers==4.38.1) (2025.1.31)
2025-03-14T04:46:11.1460721Z Downloading transformers-4.38.1-py3-none-any.whl (8.5 MB)
2025-03-14T04:46:11.2051889Z [?25l   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/8.5 MB ? eta -:--:--
2025-03-14T04:46:11.2056461Z    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.5/8.5 MB 149.8 MB/s eta 0:00:00
2025-03-14T04:46:11.2065900Z [?25hUsing cached tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)
2025-03-14T04:46:12.0305817Z Installing collected packages: tokenizers, transformers
2025-03-14T04:46:12.0306482Z   Attempting uninstall: tokenizers
2025-03-14T04:46:12.0306900Z     Found existing installation: tokenizers 0.19.1
2025-03-14T04:46:12.0364652Z     Uninstalling tokenizers-0.19.1:
2025-03-14T04:46:12.0365422Z       Successfully uninstalled tokenizers-0.19.1
2025-03-14T04:46:12.1028194Z   Attempting uninstall: transformers
2025-03-14T04:46:12.1087789Z     Found existing installation: transformers 4.44.2
2025-03-14T04:46:12.2169758Z     Uninstalling transformers-4.44.2:
2025-03-14T04:46:12.2369035Z       Successfully uninstalled transformers-4.44.2
2025-03-14T04:46:15.3287181Z Successfully installed tokenizers-0.15.2 transformers-4.38.1
2025-03-14T04:46:15.4989779Z + echo 'Print all dependencies after TorchBench is installed'
2025-03-14T04:46:15.4990213Z Print all dependencies after TorchBench is installed
2025-03-14T04:46:15.4990480Z + python -mpip freeze
2025-03-14T04:46:15.8373881Z absl-py==2.1.0
2025-03-14T04:46:15.8374454Z accelerate==1.5.1
2025-03-14T04:46:15.8374747Z aiohappyeyeballs==2.6.1
2025-03-14T04:46:15.8375047Z aiohttp==3.11.13
2025-03-14T04:46:15.8375374Z aiosignal==1.3.2
2025-03-14T04:46:15.8375612Z alabaster==0.7.16
2025-03-14T04:46:15.8375797Z annotated-types==0.7.0
2025-03-14T04:46:15.8376016Z antlr4-python3-runtime==4.9.3
2025-03-14T04:46:15.8376241Z anyascii==0.3.2
2025-03-14T04:46:15.8376424Z astroid==3.3.9
2025-03-14T04:46:15.8376604Z asttokens==3.0.0
2025-03-14T04:46:15.8376786Z astunparse==1.6.3
2025-03-14T04:46:15.8376973Z async-timeout==5.0.1
2025-03-14T04:46:15.8377164Z attrs==23.1.0
2025-03-14T04:46:15.8377345Z audioread==3.0.1
2025-03-14T04:46:15.8377530Z babel==2.17.0
2025-03-14T04:46:15.8377701Z backcall==0.2.0
2025-03-14T04:46:15.8377884Z beautifulsoup4==4.13.3
2025-03-14T04:46:15.8378737Z -e git+https://github.com/pytorch/benchmark@373ffb19dc470f4423a3176a4133f8f4b3cdb5bd#egg=bert_pytorch&subdirectory=torchbenchmark/models/BERT_pytorch
2025-03-14T04:46:15.8379232Z black==25.1.0
2025-03-14T04:46:15.8379553Z blinker==1.9.0
2025-03-14T04:46:15.8379757Z blis==1.2.0
2025-03-14T04:46:15.8380006Z blobfile==3.0.0
2025-03-14T04:46:15.8380425Z bokeh==3.4.3
2025-03-14T04:46:15.8380725Z boto3==1.35.42
2025-03-14T04:46:15.8381029Z botocore==1.35.99
2025-03-14T04:46:15.8381338Z breathe==4.34.0
2025-03-14T04:46:15.8381830Z bs4==0.0.1
2025-03-14T04:46:15.8382089Z cachetools==5.5.2
2025-03-14T04:46:15.8382934Z cardboardlint==1.3.1
2025-03-14T04:46:15.8383342Z catalogue==2.0.10
2025-03-14T04:46:15.8383542Z certifi==2025.1.31
2025-03-14T04:46:15.8383742Z cffi==1.17.1
2025-03-14T04:46:15.8383958Z charset-normalizer==3.4.1
2025-03-14T04:46:15.8384262Z click==8.1.8
2025-03-14T04:46:15.8384460Z cloudpathlib==0.21.0
2025-03-14T04:46:15.8384661Z cloudpickle==3.1.1
2025-03-14T04:46:15.8384860Z colorama==0.4.6
2025-03-14T04:46:15.8385056Z comm==0.2.2
2025-03-14T04:46:15.8385310Z confection==0.1.5
2025-03-14T04:46:15.8385510Z contourpy==1.2.1
2025-03-14T04:46:15.8385702Z coremltools==5.0b5
2025-03-14T04:46:15.8385895Z cryptography==44.0.2
2025-03-14T04:46:15.8386091Z csvw==3.5.1
2025-03-14T04:46:15.8386270Z cycler==0.12.1
2025-03-14T04:46:15.8386481Z cymem==2.0.11
2025-03-14T04:46:15.8386669Z Cython==3.0.12
2025-03-14T04:46:15.8386850Z DALL-E==0.1
2025-03-14T04:46:15.8387035Z dataclasses-json==0.6.7
2025-03-14T04:46:15.8387245Z datasets==3.3.2
2025-03-14T04:46:15.8387429Z debugpy==1.8.13
2025-03-14T04:46:15.8387613Z decorator==5.2.1
2025-03-14T04:46:15.8387804Z defusedxml==0.7.1
2025-03-14T04:46:15.8387999Z Deprecated==1.2.18
2025-03-14T04:46:15.8388425Z detectron2 @ git+https://github.com/facebookresearch/detectron2.git@0df2d73d0013db7de629602c23cc120219b4f2b8
2025-03-14T04:46:15.8388855Z diffusers==0.30.3
2025-03-14T04:46:15.8389047Z dill==0.3.8
2025-03-14T04:46:15.8389228Z diskcache==5.6.3
2025-03-14T04:46:15.8389417Z distro==1.9.0
2025-03-14T04:46:15.8389605Z dlinfo==2.0.0
2025-03-14T04:46:15.8389789Z dnspython==2.7.0
2025-03-14T04:46:15.8389987Z docker-pycreds==0.4.0
2025-03-14T04:46:15.8390192Z docutils==0.16
2025-03-14T04:46:15.8390379Z dominate==2.9.1
2025-03-14T04:46:15.8390751Z effdet @ git+https://github.com/rwightman/efficientdet-pytorch.git@d43c9e34cd62d22b4205831bb735f6dd83b8e881
2025-03-14T04:46:15.8391172Z einops==0.8.1
2025-03-14T04:46:15.8391371Z eval_type_backport==0.2.2
2025-03-14T04:46:15.8391597Z exceptiongroup==1.2.2
2025-03-14T04:46:15.8391801Z execnet==2.1.1
2025-03-14T04:46:15.8391988Z executing==2.2.0
2025-03-14T04:46:15.8392176Z exhale==0.2.3
2025-03-14T04:46:15.8392360Z expecttest==0.3.0
2025-03-14T04:46:15.8392560Z fastjsonschema==2.21.1
2025-03-14T04:46:15.8392764Z FastNLP==0.6.0
2025-03-14T04:46:15.8392956Z fbscribelogger==0.1.7
2025-03-14T04:46:15.8393161Z ffmpeg-python==0.2.0
2025-03-14T04:46:15.8393375Z filelock==3.16.1
2025-03-14T04:46:15.8393563Z Flask==3.1.0
2025-03-14T04:46:15.8393738Z flatbuffers==2.0
2025-03-14T04:46:15.8393930Z fonttools==4.56.0
2025-03-14T04:46:15.8394118Z frozenlist==1.5.0
2025-03-14T04:46:15.8394312Z fsspec==2024.10.0
2025-03-14T04:46:15.8394495Z ftfy==6.3.1
2025-03-14T04:46:15.8394830Z functorch @ git+https://github.com/pytorch/functorch.git@b71aa0b4387b86c278132209b99538be48ef4c74
2025-03-14T04:46:15.8395214Z future==1.0.0
2025-03-14T04:46:15.8395422Z fvcore==0.1.5.post20221221
2025-03-14T04:46:15.8395630Z gdown==5.2.0
2025-03-14T04:46:15.8395806Z ghstack==0.8.0
2025-03-14T04:46:15.8395982Z gitdb==4.0.12
2025-03-14T04:46:15.8396153Z GitPython==3.1.44
2025-03-14T04:46:15.8396338Z google-auth==2.38.0
2025-03-14T04:46:15.8396540Z google-auth-oauthlib==1.0.0
2025-03-14T04:46:15.8396742Z greenlet==3.1.1
2025-03-14T04:46:15.8396918Z grpcio==1.71.0
2025-03-14T04:46:15.8397100Z gym==0.26.2
2025-03-14T04:46:15.8397273Z gym-notices==0.0.8
2025-03-14T04:46:15.8397458Z h5py==3.13.0
2025-03-14T04:46:15.8397637Z higher==0.2.1
2025-03-14T04:46:15.8397823Z huggingface-hub==0.29.3
2025-03-14T04:46:15.8398026Z hydra-core==1.3.2
2025-03-14T04:46:15.8398496Z hypothesis==5.35.1
2025-03-14T04:46:15.8398697Z idna==3.10
2025-03-14T04:46:15.8398878Z imageio==2.37.0
2025-03-14T04:46:15.8399063Z imagesize==1.4.1
2025-03-14T04:46:15.8399260Z importlib_metadata==8.6.1
2025-03-14T04:46:15.8399461Z inflect==7.5.0
2025-03-14T04:46:15.8399728Z iniconfig==2.0.0
2025-03-14T04:46:15.8399907Z iopath==0.1.9
2025-03-14T04:46:15.8400086Z ipykernel==6.29.5
2025-03-14T04:46:15.8400303Z ipython==8.12.0
2025-03-14T04:46:15.8400482Z isodate==0.7.2
2025-03-14T04:46:15.8400665Z isort==6.0.1
2025-03-14T04:46:15.8400843Z itsdangerous==2.2.0
2025-03-14T04:46:15.8401033Z jedi==0.19.2
2025-03-14T04:46:15.8401207Z Jinja2==3.1.6
2025-03-14T04:46:15.8401385Z jmespath==1.0.1
2025-03-14T04:46:15.8401564Z joblib==1.4.2
2025-03-14T04:46:15.8401740Z jsonpatch==1.33
2025-03-14T04:46:15.8401921Z jsonpointer==3.0.0
2025-03-14T04:46:15.8402103Z jsonschema==4.23.0
2025-03-14T04:46:15.8402308Z jsonschema-specifications==2024.10.1
2025-03-14T04:46:15.8402553Z junitparser==2.1.1
2025-03-14T04:46:15.8402754Z jupyter-cache==0.6.1
2025-03-14T04:46:15.8402952Z jupyter_client==8.6.3
2025-03-14T04:46:15.8403142Z jupyter_core==5.7.2
2025-03-14T04:46:15.8403330Z kaldi-io==0.9.8
2025-03-14T04:46:15.8403513Z kiwisolver==1.4.7
2025-03-14T04:46:15.8403690Z kornia==0.8.0
2025-03-14T04:46:15.8403869Z kornia_rs==0.1.8
2025-03-14T04:46:15.8404048Z lameenc==1.8.1
2025-03-14T04:46:15.8404225Z langcodes==3.5.0
2025-03-14T04:46:15.8404401Z langdetect==1.0.9
2025-03-14T04:46:15.8404580Z language-tags==1.2.0
2025-03-14T04:46:15.8404782Z language_data==1.3.0
2025-03-14T04:46:15.8404966Z lark==0.12.0
2025-03-14T04:46:15.8405142Z lazy_loader==0.4
2025-03-14T04:46:15.8405315Z libcst==1.7.0
2025-03-14T04:46:15.8405488Z librosa==0.9.2
2025-03-14T04:46:15.8405669Z lintrunner==0.12.7
2025-03-14T04:46:15.8405848Z llvmlite==0.38.1
2025-03-14T04:46:15.8406026Z lxml==5.3.0
2025-03-14T04:46:15.8406207Z marisa-trie==1.2.1
2025-03-14T04:46:15.8406390Z Markdown==3.7
2025-03-14T04:46:15.8406579Z markdown-it-py==2.2.0
2025-03-14T04:46:15.8406789Z MarkupSafe==3.0.2
2025-03-14T04:46:15.8406973Z marshmallow==3.26.1
2025-03-14T04:46:15.8407151Z matplotlib==3.5.3
2025-03-14T04:46:15.8407342Z matplotlib-inline==0.1.7
2025-03-14T04:46:15.8407553Z mccabe==0.7.0
2025-03-14T04:46:15.8407737Z mdit-py-plugins==0.3.5
2025-03-14T04:46:15.8407930Z mdurl==0.1.2
2025-03-14T04:46:15.8408099Z ml_dtypes==0.5.1
2025-03-14T04:46:15.8408275Z MonkeyType==23.3.0
2025-03-14T04:46:15.8408456Z more-itertools==10.6.0
2025-03-14T04:46:15.8408642Z mpmath==1.3.0
2025-03-14T04:46:15.8408812Z msgpack==1.1.0
2025-03-14T04:46:15.8408984Z multidict==6.1.0
2025-03-14T04:46:15.8409161Z multiprocess==0.70.16
2025-03-14T04:46:15.8409350Z murmurhash==1.0.12
2025-03-14T04:46:15.8409514Z musdb==0.4.2
2025-03-14T04:46:15.8409685Z museval==0.4.1
2025-03-14T04:46:15.8409860Z mypy==1.14.0
2025-03-14T04:46:15.8410044Z mypy-extensions==1.0.0
2025-03-14T04:46:15.8410232Z myst-nb==0.17.2
2025-03-14T04:46:15.8410416Z myst-parser==0.18.1
2025-03-14T04:46:15.8410597Z nbclient==0.7.4
2025-03-14T04:46:15.8410777Z nbformat==5.10.4
2025-03-14T04:46:15.8410954Z nest-asyncio==1.6.0
2025-03-14T04:46:15.8411133Z networkx==2.8.8
2025-03-14T04:46:15.8411312Z ninja==1.10.2
2025-03-14T04:46:15.8411482Z nose==1.3.7
2025-03-14T04:46:15.8411643Z numba==0.55.2
2025-03-14T04:46:15.8411812Z numpy==1.22.4
2025-03-14T04:46:15.8411996Z nvidia-cublas-cu12==12.4.5.8
2025-03-14T04:46:15.8412224Z nvidia-cuda-cupti-cu12==12.4.127
2025-03-14T04:46:15.8412460Z nvidia-cuda-nvrtc-cu12==12.4.127
2025-03-14T04:46:15.8412687Z nvidia-cuda-runtime-cu12==12.4.127
2025-03-14T04:46:15.8412914Z nvidia-cudnn-cu12==9.1.0.70
2025-03-14T04:46:15.8413121Z nvidia-cufft-cu12==11.2.1.3
2025-03-14T04:46:15.8413327Z nvidia-curand-cu12==10.3.5.147
2025-03-14T04:46:15.8413544Z nvidia-cusolver-cu12==11.6.1.9
2025-03-14T04:46:15.8413760Z nvidia-cusparse-cu12==12.3.1.170
2025-03-14T04:46:15.8413984Z nvidia-cusparselt-cu12==0.6.2
2025-03-14T04:46:15.8414198Z nvidia-ml-py==12.570.86
2025-03-14T04:46:15.8414399Z nvidia-nccl-cu12==2.21.5
2025-03-14T04:46:15.8414683Z nvidia-nvjitlink-cu12==12.4.127
2025-03-14T04:46:15.8414916Z nvidia-nvtx-cu12==12.4.127
2025-03-14T04:46:15.8415113Z oauthlib==3.2.2
2025-03-14T04:46:15.8415299Z omegaconf==2.3.0
2025-03-14T04:46:15.8415478Z onnx==1.17.0
2025-03-14T04:46:15.8415655Z onnxscript==0.2.2
2025-03-14T04:46:15.8415891Z opacus==1.5.3
2025-03-14T04:46:15.8416071Z opencv-python==4.8.0.74
2025-03-14T04:46:15.8416263Z opt-einsum==3.3.0
2025-03-14T04:46:15.8416442Z optree==0.13.0
2025-03-14T04:46:15.8416621Z packaging==24.2
2025-03-14T04:46:15.8416811Z pandas==2.0.3
2025-03-14T04:46:15.8416986Z parameterized==0.8.1
2025-03-14T04:46:15.8417169Z parso==0.8.4
2025-03-14T04:46:15.8417338Z patch==1.16
2025-03-14T04:46:15.8417501Z pathspec==0.12.1
2025-03-14T04:46:15.8417681Z pexpect==4.9.0
2025-03-14T04:46:15.8417857Z phonemizer==3.3.0
2025-03-14T04:46:15.8418041Z pickleshare==0.7.5
2025-03-14T04:46:15.8418221Z pillow==11.0.0
2025-03-14T04:46:15.8418398Z platformdirs==4.3.6
2025-03-14T04:46:15.8418579Z pluggy==1.5.0
2025-03-14T04:46:15.8418755Z ply==3.11
2025-03-14T04:46:15.8418921Z pooch==1.8.2
2025-03-14T04:46:15.8419095Z portalocker==3.1.1
2025-03-14T04:46:15.8419275Z preshed==3.0.9
2025-03-14T04:46:15.8419446Z prettytable==3.15.1
2025-03-14T04:46:15.8419630Z prompt_toolkit==3.0.50
2025-03-14T04:46:15.8419827Z propcache==0.3.0
2025-03-14T04:46:15.8419998Z protobuf==3.20.2
2025-03-14T04:46:15.8420168Z psutil==7.0.0
2025-03-14T04:46:15.8420337Z ptyprocess==0.7.0
2025-03-14T04:46:15.8420510Z PuLP==2.9.0
2025-03-14T04:46:15.8420681Z pure_eval==0.2.3
2025-03-14T04:46:15.8420859Z pwlf==2.2.1
2025-03-14T04:46:15.8421027Z py-cpuinfo==9.0.0
2025-03-14T04:46:15.8421201Z pyaml==25.1.0
2025-03-14T04:46:15.8421370Z pyarrow==19.0.1
2025-03-14T04:46:15.8421539Z pyasn1==0.6.1
2025-03-14T04:46:15.8421714Z pyasn1_modules==0.4.1
2025-03-14T04:46:15.8421903Z pyclipper==1.3.0.post6
2025-03-14T04:46:15.8422090Z pycocotools==2.0.8
2025-03-14T04:46:15.8422263Z pycparser==2.22
2025-03-14T04:46:15.8422447Z pycryptodomex==3.21.0
2025-03-14T04:46:15.8422637Z pydantic==2.10.6
2025-03-14T04:46:15.8422822Z pydantic_core==2.27.2
2025-03-14T04:46:15.8423007Z pyDOE==0.3.8
2025-03-14T04:46:15.8423179Z pydot==3.0.4
2025-03-14T04:46:15.8423351Z pygame==2.6.1
2025-03-14T04:46:15.8423520Z PyGithub==2.3.0
2025-03-14T04:46:15.8423696Z Pygments==2.15.0
2025-03-14T04:46:15.8423888Z PyJWT==2.10.1
2025-03-14T04:46:15.8424174Z pylint==3.3.5
2025-03-14T04:46:15.8424376Z PyNaCl==1.5.0
2025-03-14T04:46:15.8424558Z pynvml==12.0.0
2025-03-14T04:46:15.8424739Z pyparsing==3.2.1
2025-03-14T04:46:15.8424935Z pypdfium2==4.30.1
2025-03-14T04:46:15.8425129Z pysbd==0.3.4
2025-03-14T04:46:15.8425315Z PySocks==1.7.1
2025-03-14T04:46:15.8425485Z pytest==8.3.5
2025-03-14T04:46:15.8425664Z pytest-benchmark==5.1.0
2025-03-14T04:46:15.8425867Z pytest-cpp==2.3.0
2025-03-14T04:46:15.8426072Z pytest-flakefinder==1.1.0
2025-03-14T04:46:15.8426302Z pytest-rerunfailures==14.0
2025-03-14T04:46:15.8426530Z pytest-subtests==0.13.1
2025-03-14T04:46:15.8426741Z pytest-xdist==3.3.1
2025-03-14T04:46:15.8426959Z python-dateutil==2.9.0.post0
2025-03-14T04:46:15.8427184Z python-doctr==0.10.0
2025-03-14T04:46:15.8427387Z python-etcd==0.4.5
2025-03-14T04:46:15.8427886Z pytorch-labs-segment-anything-fast @ git+https://github.com/pytorch-labs/segment-anything-fast.git@e6aadeb86f3ae1f58c3f98e2a91e251716e0f2aa
2025-03-14T04:46:15.8428632Z -e git+https://github.com/pytorch/pytorch_sphinx_theme.git@4125c834e1aa0945fde6ef58ff2f77f7abedc460#egg=pytorch_sphinx_theme
2025-03-14T04:46:15.8429093Z pytz==2025.1
2025-03-14T04:46:15.8429279Z PyWavelets==1.4.1
2025-03-14T04:46:15.8429474Z PyYAML==6.0.2
2025-03-14T04:46:15.8429658Z pyzmq==26.3.0
2025-03-14T04:46:15.8429861Z pyzstd==0.16.2
2025-03-14T04:46:15.8430045Z RapidFuzz==3.12.2
2025-03-14T04:46:15.8430237Z rdflib==7.1.3
2025-03-14T04:46:15.8430414Z redis==5.2.1
2025-03-14T04:46:15.8430600Z referencing==0.36.2
2025-03-14T04:46:15.8430790Z regex==2024.11.6
2025-03-14T04:46:15.8430979Z requests==2.32.3
2025-03-14T04:46:15.8431172Z requests-oauthlib==2.0.0
2025-03-14T04:46:15.8431573Z resampy==0.4.3
2025-03-14T04:46:15.8431804Z rfc3986==1.5.0
2025-03-14T04:46:15.8432000Z rich==13.9.4
2025-03-14T04:46:15.8432189Z rpds-py==0.23.1
2025-03-14T04:46:15.8432379Z rsa==4.9
2025-03-14T04:46:15.8432564Z s3transfer==0.10.4
2025-03-14T04:46:15.8432759Z safetensors==0.5.3
2025-03-14T04:46:15.8433016Z scikit-image==0.19.3
2025-03-14T04:46:15.8433220Z scikit-learn==1.6.1
2025-03-14T04:46:15.8433413Z scipy==1.10.1
2025-03-14T04:46:15.8433597Z segments==2.3.0
2025-03-14T04:46:15.8433802Z sentencepiece==0.2.0
2025-03-14T04:46:15.8433985Z sentry-sdk==2.22.0
2025-03-14T04:46:15.8434162Z setproctitle==1.3.5
2025-03-14T04:46:15.8434348Z shapely==2.0.7
2025-03-14T04:46:15.8434513Z shellingham==1.5.4
2025-03-14T04:46:15.8434693Z simplejson==3.20.1
2025-03-14T04:46:15.8434869Z six==1.17.0
2025-03-14T04:46:15.8435034Z smart-open==7.1.0
2025-03-14T04:46:15.8435263Z smmap==5.0.2
2025-03-14T04:46:15.8435469Z snowballstemmer==2.2.0
2025-03-14T04:46:15.8435662Z sortedcontainers==2.4.0
2025-03-14T04:46:15.8435858Z soundfile==0.13.1
2025-03-14T04:46:15.8436037Z soupsieve==2.6
2025-03-14T04:46:15.8436208Z soxr==0.5.0.post1
2025-03-14T04:46:15.8436380Z spacy==3.8.3
2025-03-14T04:46:15.8436549Z spacy-legacy==3.0.12
2025-03-14T04:46:15.8436733Z spacy-loggers==1.0.5
2025-03-14T04:46:15.8436903Z Sphinx==5.3.0
2025-03-14T04:46:15.8437088Z sphinx-copybutton==0.5.0
2025-03-14T04:46:15.8437286Z sphinx-panels==0.4.1
2025-03-14T04:46:15.8437483Z sphinxcontrib-applehelp==2.0.0
2025-03-14T04:46:15.8437706Z sphinxcontrib-devhelp==2.0.0
2025-03-14T04:46:15.8437922Z sphinxcontrib-htmlhelp==2.1.0
2025-03-14T04:46:15.8438136Z sphinxcontrib-jsmath==1.0.1
2025-03-14T04:46:15.8438343Z sphinxcontrib-katex==0.8.6
2025-03-14T04:46:15.8438548Z sphinxcontrib-qthelp==2.0.0
2025-03-14T04:46:15.8438769Z sphinxcontrib-serializinghtml==2.0.0
2025-03-14T04:46:15.8438994Z SQLAlchemy==2.0.39
2025-03-14T04:46:15.8439169Z srsly==2.5.1
2025-03-14T04:46:15.8439338Z stack-data==0.6.3
2025-03-14T04:46:15.8439513Z stempeg==0.2.3
2025-03-14T04:46:15.8439684Z submitit==1.5.2
2025-03-14T04:46:15.8439857Z sympy==1.13.3
2025-03-14T04:46:15.8440028Z tabulate==0.9.0
2025-03-14T04:46:15.8440205Z tb-nightly==2.13.0a20230426
2025-03-14T04:46:15.8440400Z tensorboard==2.13.0
2025-03-14T04:46:15.8440590Z tensorboard-data-server==0.7.2
2025-03-14T04:46:15.8440804Z tensorboardX==2.6.2.2
2025-03-14T04:46:15.8440991Z termcolor==2.5.0
2025-03-14T04:46:15.8441166Z thinc==8.3.4
2025-03-14T04:46:15.8441337Z threadpoolctl==3.6.0
2025-03-14T04:46:15.8441519Z thriftpy2==0.5.2
2025-03-14T04:46:15.8441694Z tifffile==2024.8.30
2025-03-14T04:46:15.8442045Z timm @ git+https://github.com/huggingface/pytorch-image-models.git@730b907b4d45a4713cbc425cbf224c46089fd514
2025-03-14T04:46:15.8442413Z tlparse==0.3.30
2025-03-14T04:46:15.8442589Z tokenizers==0.15.2
2025-03-14T04:46:15.8442753Z tomli==2.2.1
2025-03-14T04:46:15.8442924Z tomlkit==0.13.2
2025-03-14T04:46:15.8443425Z torch @ file:///var/lib/jenkins/workspace/dist/torch-2.8.0a0%2Bgitaed0b7a-cp39-cp39-linux_x86_64.whl#sha256=70b47dc351bf5a859f8225ee88934d219f3ed496ecea95a101a28f8ac92d2f63
2025-03-14T04:46:15.8444109Z torch_geometric @ git+https://github.com/pyg-team/pytorch_geometric.git@cabcd4097442ba60aa1efa11e1619dd9bb8fb527
2025-03-14T04:46:15.8444573Z torchao @ git+https://github.com/pytorch/ao.git@9259584f98db0760b27492a63050a2915c753dbe
2025-03-14T04:46:15.8445003Z torchaudio @ git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-14T04:46:15.8445507Z torchmultimodal @ git+https://github.com/facebookresearch/multimodal.git@6569fcc03450c2360b50d772bf9b18ec3487fcf4
2025-03-14T04:46:15.8446013Z torchvision @ git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-14T04:46:15.8446352Z tornado==6.4.2
2025-03-14T04:46:15.8446531Z tqdm==4.67.1
2025-03-14T04:46:15.8446695Z traitlets==5.14.3
2025-03-14T04:46:15.8446872Z transformers==4.38.1
2025-03-14T04:46:15.8447050Z treetable==0.2.5
2025-03-14T04:46:15.8447250Z triton @ file:///var/lib/jenkins/triton/python
2025-03-14T04:46:15.8447588Z typeguard==4.4.2
2025-03-14T04:46:15.8447773Z typer==0.15.2
2025-03-14T04:46:15.8447951Z typing-inspect==0.9.0
2025-03-14T04:46:15.8448150Z typing_extensions==4.12.2
2025-03-14T04:46:15.8448346Z tzdata==2025.1
2025-03-14T04:46:15.8448511Z Unidecode==1.3.8
2025-03-14T04:46:15.8448751Z unittest-xml-reporting==3.2.0
2025-03-14T04:46:15.8448971Z uritemplate==4.1.1
2025-03-14T04:46:15.8449142Z urllib3==1.26.20
2025-03-14T04:46:15.8449309Z visdom==0.2.4
2025-03-14T04:46:15.8449473Z wandb==0.19.8
2025-03-14T04:46:15.8449637Z wasabi==1.1.3
2025-03-14T04:46:15.8449799Z wcwidth==0.2.13
2025-03-14T04:46:15.8449966Z weasel==0.4.1
2025-03-14T04:46:15.8450135Z websocket-client==1.8.0
2025-03-14T04:46:15.8450319Z Werkzeug==3.1.3
2025-03-14T04:46:15.8450488Z wrapt==1.17.2
2025-03-14T04:46:15.8450649Z xdoctest==1.1.0
2025-03-14T04:46:15.8450806Z xxhash==3.5.0
2025-03-14T04:46:15.8450973Z xyzservices==2025.1.0
2025-03-14T04:46:15.8451153Z yacs==0.1.8
2025-03-14T04:46:15.8451317Z yarl==1.18.3
2025-03-14T04:46:15.8451486Z z3-solver==4.12.6.0
2025-03-14T04:46:15.8451660Z zipp==3.21.0
2025-03-14T04:46:15.8821392Z + popd
2025-03-14T04:46:15.8866500Z ~/workspace
2025-03-14T04:46:15.8883636Z + [[ cpu_inductor_torchbench != *cpu* ]]
2025-03-14T04:46:15.8946024Z ++ pwd
2025-03-14T04:46:15.8946391Z + PYTHONPATH=/var/lib/jenkins/workspace/torchbench
2025-03-14T04:46:15.8946744Z + test_dynamo_benchmark torchbench 0
2025-03-14T04:46:15.8946961Z ++ pwd
2025-03-14T04:46:15.8947191Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:46:15.8947466Z + local suite=torchbench
2025-03-14T04:46:15.8947658Z + shift
2025-03-14T04:46:15.8947821Z + local shard_id=0
2025-03-14T04:46:15.8947994Z + shift
2025-03-14T04:46:15.8948189Z + [[ cpu_inductor_torchbench == *perf_compare* ]]
2025-03-14T04:46:15.8948440Z + [[ cpu_inductor_torchbench == *perf* ]]
2025-03-14T04:46:15.8948671Z + [[ cpu_inductor_torchbench == *cpu* ]]
2025-03-14T04:46:15.8948902Z + local dt=float32
2025-03-14T04:46:15.8951386Z + [[ cpu_inductor_torchbench == *amp* ]]
2025-03-14T04:46:15.8954920Z + [[ cpu_inductor_torchbench == *freezing* ]]
2025-03-14T04:46:15.8983040Z + test_single_dynamo_benchmark inference torchbench 0 --inference --float32
2025-03-14T04:46:15.8985669Z ++ pwd
2025-03-14T04:46:15.8986073Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:46:15.8986652Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports
2025-03-14T04:46:15.8986967Z + local name=inference
2025-03-14T04:46:15.8987215Z + shift
2025-03-14T04:46:15.8987409Z + local suite=torchbench
2025-03-14T04:46:15.8987636Z + shift
2025-03-14T04:46:15.8987856Z + local shard_id=0
2025-03-14T04:46:15.8988081Z + shift
2025-03-14T04:46:15.8988299Z + partition_flags=()
2025-03-14T04:46:15.8988547Z + local partition_flags
2025-03-14T04:46:15.8988771Z + [[ -n 2 ]]
2025-03-14T04:46:15.8989003Z + [[ -n 0 ]]
2025-03-14T04:46:15.8989359Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id")
2025-03-14T04:46:15.8989778Z + [[ cpu_inductor_torchbench == *perf_compare* ]]
2025-03-14T04:46:15.8990046Z + [[ cpu_inductor_torchbench == *perf* ]]
2025-03-14T04:46:15.8990294Z + [[ cpu_inductor_torchbench == *_avx2* ]]
2025-03-14T04:46:15.8990554Z + [[ cpu_inductor_torchbench == *_avx512* ]]
2025-03-14T04:46:15.8991298Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --device cpu --inference --float32 --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv
2025-03-14T04:46:21.2706719Z 
2025-03-14T04:46:23.5812030Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:46:23.5903691Z loading model: 0it [00:02, ?it/s]
2025-03-14T04:46:23.5904766Z cpu  eval  BERT_pytorch                       
2025-03-14T04:46:24.8667963Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:25.4279029Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:26.3547367Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:49.6233206Z Compilation time (from dynamo_timed): 21.041204989
2025-03-14T04:46:49.6288151Z pass
2025-03-14T04:46:49.6290340Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:49.6291978Z TIMING: _recursive_pre_grad_passes:0.00817 _recursive_joint_graph_passes:0.3852 _recursive_post_grad_passes:0.11992 async_compile.wait:1.67509 code_gen:13.82744 inductor_compile:15.35056 backend_compile:18.49922 entire_frame_compile:21.0412 gc:0.00231 total_wall_time:21.0412
2025-03-14T04:46:49.6293139Z STATS: call_* op count: 543 | FakeTensor.__torch_dispatch__:1438 | FakeTensorMode.__torch_dispatch__:15555 | ProxyTorchDispatchMode.__torch_dispatch__:6318
2025-03-14T04:46:49.6293720Z Dynamo produced 1 graphs covering 543 ops with 0 graph breaks (0 unique)
2025-03-14T04:46:54.5734692Z 
2025-03-14T04:46:58.6323199Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:46:58.6324482Z loading model: 0it [00:04, ?it/s]
2025-03-14T04:46:58.6333330Z cpu  eval  Background_Matting                 
2025-03-14T04:46:58.6890422Z Compilation time (from dynamo_timed): 0
2025-03-14T04:46:58.6894801Z pass_due_to_skip
2025-03-14T04:46:58.6897120Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:46:58.6897630Z TIMING: total_wall_time:0
2025-03-14T04:46:58.6902158Z STATS: call_* op count: 0
2025-03-14T04:46:58.6906806Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T04:47:02.4792921Z 
2025-03-14T04:47:04.7372926Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:47:04.7373377Z loading model: 0it [00:02, ?it/s]
2025-03-14T04:47:04.7373738Z cpu  eval  LearningToPaint                    
2025-03-14T04:47:04.9123224Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:05.0246963Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:05.0713267Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:17.7977202Z Compilation time (from dynamo_timed): 11.838154129
2025-03-14T04:47:17.7977611Z pass
2025-03-14T04:47:17.7977990Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:17.7978868Z TIMING: _recursive_pre_grad_passes:0.0055 _recursive_joint_graph_passes:0.10612 _recursive_post_grad_passes:0.03381 async_compile.wait:1.61489 code_gen:8.78148 inductor_compile:9.25901 backend_compile:10.76549 entire_frame_compile:11.83815 gc:0.00126 total_wall_time:11.83815
2025-03-14T04:47:17.7980032Z STATS: call_* op count: 71 | FakeTensorMode.__torch_dispatch__:4376 | ProxyTorchDispatchMode.__torch_dispatch__:1906 | FakeTensor.__torch_dispatch__:590
2025-03-14T04:47:17.7980634Z Dynamo produced 1 graphs covering 71 ops with 0 graph breaks (0 unique)
2025-03-14T04:47:22.6622389Z 
2025-03-14T04:47:23.5293574Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth
2025-03-14T04:47:23.5550306Z 
2025-03-14T04:47:23.5551917Z 
2025-03-14T04:47:23.6552686Z   0% 0.00/528M [00:00<?, ?B/s]
2025-03-14T04:47:23.6552967Z 
2025-03-14T04:47:23.7556666Z   8% 44.4M/528M [00:00<00:01, 463MB/s]
2025-03-14T04:47:23.7556920Z 
2025-03-14T04:47:23.8557521Z  18% 95.2M/528M [00:00<00:00, 504MB/s]
2025-03-14T04:47:23.8557771Z 
2025-03-14T04:47:23.9558226Z  28% 147M/528M [00:00<00:00, 522MB/s] 
2025-03-14T04:47:23.9560340Z 
2025-03-14T04:47:24.0560450Z  38% 200M/528M [00:00<00:00, 533MB/s]
2025-03-14T04:47:24.0560667Z 
2025-03-14T04:47:24.1560030Z  48% 252M/528M [00:00<00:00, 540MB/s]
2025-03-14T04:47:24.1560474Z 
2025-03-14T04:47:24.2565313Z  58% 305M/528M [00:00<00:00, 544MB/s]
2025-03-14T04:47:24.2566914Z 
2025-03-14T04:47:24.3562792Z  68% 358M/528M [00:00<00:00, 549MB/s]
2025-03-14T04:47:24.3563031Z 
2025-03-14T04:47:24.4565181Z  78% 412M/528M [00:00<00:00, 552MB/s]
2025-03-14T04:47:24.4565774Z 
2025-03-14T04:47:24.5563899Z  88% 464M/528M [00:00<00:00, 552MB/s]
2025-03-14T04:47:24.5565001Z 
2025-03-14T04:47:24.5757359Z  98% 518M/528M [00:01<00:00, 553MB/s]
2025-03-14T04:47:24.5759193Z 100% 528M/528M [00:01<00:00, 542MB/s]
2025-03-14T04:47:27.9083710Z 
2025-03-14T04:47:27.9084624Z loading model: 0it [00:05, ?it/s]
2025-03-14T04:47:27.9085071Z cpu  eval  Super_SloMo                        
2025-03-14T04:47:54.2530942Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:47:57.4659458Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:00.7183313Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:38.0522707Z Compilation time (from dynamo_timed): 33.48430264
2025-03-14T04:48:38.0524609Z pass
2025-03-14T04:48:38.0532875Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:38.0538074Z TIMING: _recursive_pre_grad_passes:0.00759 _recursive_joint_graph_passes:0.21449 _recursive_post_grad_passes:0.11769 async_compile.wait:2.85409 code_gen:26.03577 inductor_compile:28.49531 backend_compile:31.51366 entire_frame_compile:33.4843 gc:0.00137 total_wall_time:33.4843
2025-03-14T04:48:38.0540109Z STATS: call_* op count: 374 | FakeTensorMode.__torch_dispatch__:15567 | ProxyTorchDispatchMode.__torch_dispatch__:6332 | FakeTensor.__torch_dispatch__:2521
2025-03-14T04:48:38.0540660Z Dynamo produced 1 graphs covering 374 ops with 0 graph breaks (0 unique)
2025-03-14T04:48:43.9240888Z 
2025-03-14T04:48:44.3392500Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth
2025-03-14T04:48:44.3649653Z 
2025-03-14T04:48:44.3654693Z 
2025-03-14T04:48:44.4653268Z   0% 0.00/233M [00:00<?, ?B/s]
2025-03-14T04:48:44.4653535Z 
2025-03-14T04:48:44.5653900Z  23% 54.4M/233M [00:00<00:00, 569MB/s]
2025-03-14T04:48:44.5654184Z 
2025-03-14T04:48:44.6791863Z  48% 111M/233M [00:00<00:00, 584MB/s] 
2025-03-14T04:48:44.6799955Z 
2025-03-14T04:48:44.7791319Z  72% 167M/233M [00:00<00:00, 549MB/s]
2025-03-14T04:48:44.7791569Z 
2025-03-14T04:48:44.7970391Z  97% 226M/233M [00:00<00:00, 574MB/s]
2025-03-14T04:48:44.7971492Z 100% 233M/233M [00:00<00:00, 570MB/s]
2025-03-14T04:48:45.2439801Z 
2025-03-14T04:48:45.2440383Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:48:45.2440790Z cpu  eval  alexnet                            
2025-03-14T04:48:45.7625399Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:46.1860421Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:46.5256423Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:59.6601869Z Compilation time (from dynamo_timed): 11.749857364
2025-03-14T04:48:59.6602204Z pass
2025-03-14T04:48:59.6602569Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:48:59.6603368Z TIMING: _recursive_pre_grad_passes:0.00357 _recursive_joint_graph_passes:0.10699 _recursive_post_grad_passes:0.017 async_compile.wait:1.76118 code_gen:10.48274 inductor_compile:10.61648 backend_compile:11.44721 entire_frame_compile:11.74986 gc:0.00035 total_wall_time:11.74986
2025-03-14T04:48:59.6604285Z STATS: call_* op count: 22 | FakeTensorMode.__torch_dispatch__:1073 | ProxyTorchDispatchMode.__torch_dispatch__:243 | FakeTensor.__torch_dispatch__:36
2025-03-14T04:48:59.6604800Z Dynamo produced 1 graphs covering 22 ops with 0 graph breaks (0 unique)
2025-03-14T04:49:04.5823594Z 
2025-03-14T04:49:06.3015399Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:49:06.3015871Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:49:06.3016289Z cpu  eval  basic_gnn_edgecnn                  
2025-03-14T04:49:06.9351095Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:07.3924521Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:07.7896854Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:17.3476875Z Compilation time (from dynamo_timed): 9.24955192
2025-03-14T04:49:17.3478224Z pass
2025-03-14T04:49:17.3479568Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:17.3480448Z TIMING: _recursive_pre_grad_passes:0.00422 _recursive_joint_graph_passes:0.11123 _recursive_post_grad_passes:0.01865 async_compile.wait:1.36883 code_gen:8.01698 inductor_compile:8.12065 backend_compile:8.90554 entire_frame_compile:9.24955 gc:0.00045 total_wall_time:9.24955
2025-03-14T04:49:17.3485047Z STATS: call_* op count: 55 | FakeTensorMode.__torch_dispatch__:1339 | ProxyTorchDispatchMode.__torch_dispatch__:429 | FakeTensor.__torch_dispatch__:76
2025-03-14T04:49:17.3502426Z Dynamo produced 1 graphs covering 55 ops with 0 graph breaks (0 unique)
2025-03-14T04:49:22.4940066Z 
2025-03-14T04:49:24.4308813Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:49:24.4309159Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:49:24.4317343Z cpu  eval  basic_gnn_gcn                      
2025-03-14T04:49:24.7387256Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:25.0554918Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:25.2587774Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:38.2057706Z Compilation time (from dynamo_timed): 12.469903205
2025-03-14T04:49:38.2059509Z pass
2025-03-14T04:49:38.2060031Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:38.2061433Z TIMING: entire_frame_compile:12.4699 gc:0.00766 _recursive_pre_grad_passes:0.00688 _recursive_joint_graph_passes:0.11648 _recursive_post_grad_passes:0.02121 async_compile.wait:4.86404 code_gen:10.94365 inductor_compile:11.05011 backend_compile:11.89441 total_wall_time:12.4699
2025-03-14T04:49:38.2063113Z STATS: call_* op count: 57 | FakeTensorMode.__torch_dispatch__:1046 | FakeTensor.__torch_dispatch__:59 | ProxyTorchDispatchMode.__torch_dispatch__:220
2025-03-14T04:49:38.2063988Z Dynamo produced 6 graphs covering 57 ops with 6 graph breaks (1 unique)
2025-03-14T04:49:43.0350669Z 
2025-03-14T04:49:44.6729194Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:49:44.6729534Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:49:44.6729812Z cpu  eval  basic_gnn_gin                      
2025-03-14T04:49:44.7708122Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:44.8243278Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:44.8758607Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:53.0797781Z Compilation time (from dynamo_timed): 8.103639734
2025-03-14T04:49:53.0803198Z pass
2025-03-14T04:49:53.0805516Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:53.0806448Z TIMING: _recursive_pre_grad_passes:0.00368 _recursive_joint_graph_passes:0.10117 _recursive_post_grad_passes:0.01642 async_compile.wait:1.2494 code_gen:6.96633 inductor_compile:7.05166 backend_compile:7.79057 entire_frame_compile:8.10364 gc:0.00128 total_wall_time:8.10364
2025-03-14T04:49:53.0807816Z STATS: call_* op count: 52 | FakeTensorMode.__torch_dispatch__:1249 | ProxyTorchDispatchMode.__torch_dispatch__:420 | FakeTensor.__torch_dispatch__:76
2025-03-14T04:49:53.0808452Z Dynamo produced 1 graphs covering 52 ops with 0 graph breaks (0 unique)
2025-03-14T04:49:57.3740851Z 
2025-03-14T04:49:59.0067451Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:49:59.0068416Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:49:59.0068817Z cpu  eval  basic_gnn_sage                     
2025-03-14T04:49:59.1250800Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:59.1841788Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:49:59.2378938Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:07.3167409Z Compilation time (from dynamo_timed): 7.971645604
2025-03-14T04:50:07.3167927Z pass
2025-03-14T04:50:07.3171583Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:07.3172367Z TIMING: _recursive_pre_grad_passes:0.00352 _recursive_joint_graph_passes:0.10589 _recursive_post_grad_passes:0.02763 async_compile.wait:1.15301 code_gen:6.87335 inductor_compile:6.97564 backend_compile:7.71302 entire_frame_compile:7.97165 gc:0.00049 total_wall_time:7.97165
2025-03-14T04:50:07.3173265Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:1273 | ProxyTorchDispatchMode.__torch_dispatch__:444 | FakeTensor.__torch_dispatch__:73
2025-03-14T04:50:07.3173768Z Dynamo produced 1 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-14T04:50:11.7629654Z 
2025-03-14T04:50:11.9821497Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:50:11.9821910Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:50:11.9822226Z cpu  eval  dcgan                              
2025-03-14T04:50:12.0005283Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:12.0083501Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:12.0139446Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:21.8993264Z Compilation time (from dynamo_timed): 8.896944933
2025-03-14T04:50:21.8998020Z pass
2025-03-14T04:50:21.9002907Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:21.9008768Z TIMING: _recursive_pre_grad_passes:0.00318 _recursive_joint_graph_passes:0.09963 _recursive_post_grad_passes:0.01781 async_compile.wait:1.28478 code_gen:7.73851 inductor_compile:7.86988 backend_compile:8.60951 entire_frame_compile:8.89694 gc:0.0011 total_wall_time:8.89694
2025-03-14T04:50:21.9009933Z STATS: call_* op count: 13 | FakeTensorMode.__torch_dispatch__:1006 | FakeTensor.__torch_dispatch__:107 | ProxyTorchDispatchMode.__torch_dispatch__:315
2025-03-14T04:50:21.9010496Z Dynamo produced 1 graphs covering 13 ops with 0 graph breaks (0 unique)
2025-03-14T04:50:26.2741735Z 
2025-03-14T04:50:28.1505393Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:50:28.1508929Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:50:28.1509432Z cpu  eval  demucs                             
2025-03-14T04:50:29.5794034Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:30.2079996Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:30.9146700Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:47.7722637Z Compilation time (from dynamo_timed): 14.467360103
2025-03-14T04:50:47.7727842Z pass
2025-03-14T04:50:47.7733000Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:47.7734332Z TIMING: entire_frame_compile:14.46736 gc:0.00389 _recursive_pre_grad_passes:0.00469 _recursive_joint_graph_passes:0.09592 _recursive_post_grad_passes:0.01914 async_compile.wait:4.47101 code_gen:11.99467 inductor_compile:12.22225 backend_compile:13.14226 total_wall_time:14.46736
2025-03-14T04:50:47.7735304Z STATS: call_* op count: 62 | FakeTensorMode.__torch_dispatch__:2006 | ProxyTorchDispatchMode.__torch_dispatch__:599 | FakeTensor.__torch_dispatch__:87
2025-03-14T04:50:47.7735918Z Dynamo produced 3 graphs covering 62 ops with 3 graph breaks (1 unique)
2025-03-14T04:50:52.5109332Z 
2025-03-14T04:50:53.0672479Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/densenet121-a639ec97.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/densenet121-a639ec97.pth
2025-03-14T04:50:53.0953431Z 
2025-03-14T04:50:53.0955598Z 
2025-03-14T04:50:53.1826939Z   0% 0.00/30.8M [00:00<?, ?B/s]
2025-03-14T04:50:53.1827682Z 100% 30.8M/30.8M [00:00<00:00, 488MB/s]
2025-03-14T04:50:53.9885208Z 
2025-03-14T04:50:53.9885867Z loading model: 0it [00:01, ?it/s]
2025-03-14T04:50:53.9950645Z cpu  eval  densenet121                        
2025-03-14T04:50:55.2475262Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:55.8816423Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:50:56.6510092Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:51:32.2465751Z Compilation time (from dynamo_timed): 34.268906799
2025-03-14T04:51:32.2466067Z pass
2025-03-14T04:51:32.2466384Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:51:32.2467195Z TIMING: _recursive_pre_grad_passes:0.01253 _recursive_joint_graph_passes:0.17772 _recursive_post_grad_passes:0.14418 async_compile.wait:4.34992 code_gen:18.271 inductor_compile:21.27354 backend_compile:26.97882 entire_frame_compile:34.26891 gc:0.00037 total_wall_time:34.26891
2025-03-14T04:51:32.2468205Z STATS: call_* op count: 431 | FakeTensorMode.__torch_dispatch__:24621 | ProxyTorchDispatchMode.__torch_dispatch__:10672 | FakeTensor.__torch_dispatch__:3450
2025-03-14T04:51:32.2468734Z Dynamo produced 1 graphs covering 431 ops with 0 graph breaks (0 unique)
2025-03-14T04:51:37.7895280Z 
2025-03-14T04:51:54.6243552Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:51:54.6245676Z loading model: 0it [00:16, ?it/s]
2025-03-14T04:51:54.6256816Z cpu  eval  detectron2_fasterrcnn_r_101_c4     
2025-03-14T04:52:07.5537453Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_c4. Setting accuracy check to cosine
2025-03-14T04:52:07.5686622Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:52:30.0006155Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:52:53.2520765Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:53:09.3640527Z 
2025-03-14T04:53:09.3641749Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:09.3768876Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:09.3858540Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:09.3859061Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3859812Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3860554Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3861237Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3861897Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3862543Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3863233Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3864186Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3865096Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3865898Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3866560Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3867248Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3868004Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3868729Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3869422Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3870081Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3870768Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3871517Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3872243Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3872937Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3873606Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.3874346Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3875111Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3875867Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.3876595Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.3877267Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3878031Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3878847Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3879643Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3880356Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3881033Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3882069Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3882890Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3883635Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3884363Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3885081Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3885781Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3886520Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3887218Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3887892Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3888549Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3889238Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3889972Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3890687Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3891353Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3892004Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3892755Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3893472Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3894215Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3894897Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3895551Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3896246Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3896990Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3897713Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3898410Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3899076Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3899773Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3900519Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3901243Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3901936Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3902598Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3903292Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3904037Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3904821Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3905590Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3906356Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3907073Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3907881Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3908617Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3909316Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3910005Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.3910729Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3911509Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3912270Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.3913007Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.3913679Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3914366Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3915105Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3915800Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3916468Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3917104Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3917778Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3918516Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3919235Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3919944Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3920592Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3921264Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3922007Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3922701Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3923383Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3924016Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3924683Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3925402Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3926095Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3926784Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3927446Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3928113Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3928916Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3929644Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3930314Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3930951Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3931619Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3932336Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3933030Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3933741Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3934386Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3935097Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3935818Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3936536Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3937222Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3937872Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3938591Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3939325Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3940031Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3940717Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3941403Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3942110Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3942868Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3943607Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3944415Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3945131Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3945883Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3946678Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3947469Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3948180Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3948866Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3949615Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3950353Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3951078Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3951761Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3952415Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3953092Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3953808Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3954506Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3955175Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3955823Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.3956518Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3957255Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3957978Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.3958691Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.3959361Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3960061Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3960784Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3961526Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3962200Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3962895Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3963580Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3964325Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3965048Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3965748Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3966410Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3967093Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3967829Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3968548Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3969248Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3969908Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3970596Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3971337Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3972056Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3972752Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3973417Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3974108Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3974892Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3975606Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3976323Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3976978Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3977655Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3978386Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3979102Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3979820Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3980471Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3981146Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3982402Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3983227Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3983955Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3984680Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3985393Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3986163Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3986895Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3987596Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3988261Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3988956Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3989812Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3990552Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.3991317Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.3992000Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:09.3992721Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3993484Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3994225Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.3994945Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.3995615Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:09.3996320Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.3997080Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.3997817Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.3998520Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.3999228Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:09.3999939Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4000693Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4001433Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4002141Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4002805Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4003554Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4004294Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4005036Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4005721Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4006382Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4007066Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4007799Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4008509Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4009198Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4009848Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4010527Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4011254Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4011967Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4012646Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4013294Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4013971Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4014705Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4015415Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4016096Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4016749Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4017474Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4018208Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4018955Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4019643Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4020301Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4020989Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4021736Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4022448Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4023133Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4023790Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4024554Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4025325Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4026062Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4026778Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4027464Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4028172Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4028934Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4029672Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4030372Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4031088Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4031793Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4032573Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4033304Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4034011Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4034679Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4035378Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4036133Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4036858Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4037565Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4038237Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4038911Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4039643Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4040354Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4041035Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4041687Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4042366Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4043094Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4043797Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4044531Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4045200Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4045892Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4046676Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4047392Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4048101Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4048763Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4049455Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4050202Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4050925Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4051617Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4052279Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4052969Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4053712Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4054434Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4055134Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4055777Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4056463Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4057195Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4057907Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4058625Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4059266Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4059962Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4060684Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4061381Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4062071Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4062724Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4063414Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4064214Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4064977Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4065690Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4066362Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4067060Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4067814Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4068551Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4069248Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4069914Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4070605Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4071362Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4072137Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4072847Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4073531Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4074223Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4074969Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4075684Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4076375Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4077032Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4077721Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4078466Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4079194Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4079887Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4080551Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4081246Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4082412Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4083152Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4083849Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4084518Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4085207Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4086125Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4086845Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4087533Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4088259Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4088944Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4089699Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4090438Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4091159Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4091845Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4092524Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4093270Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4093985Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4094686Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4095342Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4096040Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4096777Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4097499Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4098190Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4098848Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4099533Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4100303Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4101025Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4101765Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4102447Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4103132Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4103883Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4104693Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4105424Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4106117Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4107673Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4108520Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4109259Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4109971Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4110648Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4111779Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4112892Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4113654Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4114371Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4115056Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4116470Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4117248Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4118047Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4118765Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4119449Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4120167Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4120972Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4121721Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4122437Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4123118Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4123828Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4124598Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4125341Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4126051Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4126718Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4127412Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4128186Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4128930Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4129638Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4130352Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4131060Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4131849Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4132578Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4133285Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4133966Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4134687Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4135469Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4136529Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4137260Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4137950Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4138661Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4139425Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4140164Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4140874Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4141557Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4142543Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4143623Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4144863Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4145945Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4146906Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4148063Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4149178Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4150374Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4151377Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4152337Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4153501Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4154563Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4155764Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4156774Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4157709Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4158846Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4159915Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4160889Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4161826Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4162726Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4163626Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4164455Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4165183Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4166173Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4166860Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4167595Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4168356Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4169092Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4170042Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4170722Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4171429Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4172185Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4172927Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4173638Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4174317Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4175021Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4175778Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4176752Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4177490Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4178169Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4178869Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4179625Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4180393Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4181118Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4181967Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4182696Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4183476Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4184333Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4185071Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4185769Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4186489Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4187269Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4188011Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4188719Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4189393Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4190375Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4191142Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4191885Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4192600Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4193277Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4193983Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4194839Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4195593Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4196400Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4197094Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4197789Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4198547Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4199280Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4199989Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4200653Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4201354Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4202109Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4202845Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4203542Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4204206Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4204902Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4205660Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4206386Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4207091Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4207822Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:09.4208606Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:09.4209295Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:09.4210039Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:09.4210851Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:09.4211619Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:09.4212394Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:09.4212865Z         
2025-03-14T04:53:09.4213271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4214081Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4214710Z         
2025-03-14T04:53:09.4215076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4217134Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4218752Z         
2025-03-14T04:53:09.4219151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:09.4219647Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:09.4219921Z         
2025-03-14T04:53:09.4220407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:09.4221068Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:09.4221435Z         
2025-03-14T04:53:09.4221794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4222546Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4223093Z         
2025-03-14T04:53:09.4223498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4225529Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4227281Z         
2025-03-14T04:53:09.4227689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4228208Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:09.4228491Z         
2025-03-14T04:53:09.4228857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4229643Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4230219Z         
2025-03-14T04:53:09.4230595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4232540Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4234239Z         
2025-03-14T04:53:09.4234618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4235123Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:09.4235383Z         
2025-03-14T04:53:09.4235725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4236477Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4237089Z         
2025-03-14T04:53:09.4237449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4239313Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4240986Z         
2025-03-14T04:53:09.4241335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4242081Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.4242636Z         
2025-03-14T04:53:09.4242987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4244860Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4246564Z         
2025-03-14T04:53:09.4246944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4247435Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:09.4247706Z         
2025-03-14T04:53:09.4248091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4248593Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:09.4248872Z         
2025-03-14T04:53:09.4249222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4249969Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4250519Z         
2025-03-14T04:53:09.4250877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4252790Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4254484Z         
2025-03-14T04:53:09.4254875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4255378Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:09.4255655Z         
2025-03-14T04:53:09.4256007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4256767Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4257327Z         
2025-03-14T04:53:09.4257687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4259556Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4261169Z         
2025-03-14T04:53:09.4261540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4262027Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:09.4262293Z         
2025-03-14T04:53:09.4262636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4263373Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4263944Z         
2025-03-14T04:53:09.4264409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4266448Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4268488Z         
2025-03-14T04:53:09.4268895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4269395Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:09.4269676Z         
2025-03-14T04:53:09.4270059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4270559Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:09.4270834Z         
2025-03-14T04:53:09.4271166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4271922Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4272463Z         
2025-03-14T04:53:09.4272820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4274678Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4276366Z         
2025-03-14T04:53:09.4276754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4277255Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:09.4277527Z         
2025-03-14T04:53:09.4277875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4278641Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4279202Z         
2025-03-14T04:53:09.4279568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4281780Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4283519Z         
2025-03-14T04:53:09.4283902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4284405Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:09.4284678Z         
2025-03-14T04:53:09.4285032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4285783Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4286327Z         
2025-03-14T04:53:09.4286674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4288485Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4290096Z         
2025-03-14T04:53:09.4290456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4290952Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:09.4291224Z         
2025-03-14T04:53:09.4291600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4292101Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:09.4292374Z         
2025-03-14T04:53:09.4292710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4293441Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4293989Z         
2025-03-14T04:53:09.4294332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4296156Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4297778Z         
2025-03-14T04:53:09.4298155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4298641Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:09.4298904Z         
2025-03-14T04:53:09.4299245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4299988Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4300533Z         
2025-03-14T04:53:09.4300882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4302753Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4304510Z         
2025-03-14T04:53:09.4304894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4305408Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:09.4305681Z         
2025-03-14T04:53:09.4306026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4306790Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4307361Z         
2025-03-14T04:53:09.4307720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4309625Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4311280Z         
2025-03-14T04:53:09.4311622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4312376Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.4312943Z         
2025-03-14T04:53:09.4313301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4315208Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4316904Z         
2025-03-14T04:53:09.4317274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4317770Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:09.4318034Z         
2025-03-14T04:53:09.4318392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4318888Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:09.4319165Z         
2025-03-14T04:53:09.4319503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4320236Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4320776Z         
2025-03-14T04:53:09.4321135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4323007Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4324659Z         
2025-03-14T04:53:09.4325037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4325519Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:09.4325780Z         
2025-03-14T04:53:09.4326124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4326876Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4327431Z         
2025-03-14T04:53:09.4327787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4329622Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4331264Z         
2025-03-14T04:53:09.4331642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4332135Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:09.4332404Z         
2025-03-14T04:53:09.4332736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4333461Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4334005Z         
2025-03-14T04:53:09.4334360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4336325Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4338018Z         
2025-03-14T04:53:09.4338395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4338904Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:09.4339186Z         
2025-03-14T04:53:09.4339567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4340078Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:09.4340359Z         
2025-03-14T04:53:09.4340708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4341474Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4342039Z         
2025-03-14T04:53:09.4342398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4344346Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4346094Z         
2025-03-14T04:53:09.4346478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4346983Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:09.4347250Z         
2025-03-14T04:53:09.4347603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4348371Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4348945Z         
2025-03-14T04:53:09.4349299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4351238Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4352909Z         
2025-03-14T04:53:09.4353285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4353769Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:09.4354038Z         
2025-03-14T04:53:09.4354375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4355123Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4355679Z         
2025-03-14T04:53:09.4356031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4357871Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4359515Z         
2025-03-14T04:53:09.4359889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4360387Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:09.4360667Z         
2025-03-14T04:53:09.4361039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4361533Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:09.4361812Z         
2025-03-14T04:53:09.4362148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4362886Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4363430Z         
2025-03-14T04:53:09.4363784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4365686Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4367324Z         
2025-03-14T04:53:09.4367690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4368165Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:09.4368828Z         
2025-03-14T04:53:09.4369202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4370228Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4370798Z         
2025-03-14T04:53:09.4371166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4373051Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4374699Z         
2025-03-14T04:53:09.4375087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4375585Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:09.4375858Z         
2025-03-14T04:53:09.4376201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4376948Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4377502Z         
2025-03-14T04:53:09.4377860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4379743Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4381419Z         
2025-03-14T04:53:09.4381884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4382387Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:09.4382665Z         
2025-03-14T04:53:09.4383041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4383544Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:09.4383829Z         
2025-03-14T04:53:09.4384222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4384989Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4385549Z         
2025-03-14T04:53:09.4385916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4387833Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4389524Z         
2025-03-14T04:53:09.4389910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4390406Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:09.4390677Z         
2025-03-14T04:53:09.4391022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4391777Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4392335Z         
2025-03-14T04:53:09.4392776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4394686Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4396408Z         
2025-03-14T04:53:09.4396797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4397286Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:09.4397551Z         
2025-03-14T04:53:09.4397891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4398648Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4399206Z         
2025-03-14T04:53:09.4399563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4401440Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4403098Z         
2025-03-14T04:53:09.4403451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4404210Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.4404778Z         
2025-03-14T04:53:09.4405138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4407092Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4408847Z         
2025-03-14T04:53:09.4409224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4409720Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:09.4409992Z         
2025-03-14T04:53:09.4410376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4410880Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:09.4411143Z         
2025-03-14T04:53:09.4411489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4412246Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4412800Z         
2025-03-14T04:53:09.4413155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4415086Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4416771Z         
2025-03-14T04:53:09.4417161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4417653Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:09.4417927Z         
2025-03-14T04:53:09.4418275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4419029Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4419590Z         
2025-03-14T04:53:09.4419956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4421868Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4423547Z         
2025-03-14T04:53:09.4423930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4424489Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:09.4424759Z         
2025-03-14T04:53:09.4425115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4425882Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4426451Z         
2025-03-14T04:53:09.4426820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4428730Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4430404Z         
2025-03-14T04:53:09.4430786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4431291Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:09.4431568Z         
2025-03-14T04:53:09.4431957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4432463Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:09.4432740Z         
2025-03-14T04:53:09.4433093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4433861Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4434410Z         
2025-03-14T04:53:09.4434777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4436755Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4438452Z         
2025-03-14T04:53:09.4438836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4439339Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:09.4439605Z         
2025-03-14T04:53:09.4439959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4440686Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4441223Z         
2025-03-14T04:53:09.4441573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4443411Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4445049Z         
2025-03-14T04:53:09.4445422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4445895Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:09.4446155Z         
2025-03-14T04:53:09.4446491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4447219Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4447759Z         
2025-03-14T04:53:09.4448108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4449971Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4451670Z         
2025-03-14T04:53:09.4452050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4452547Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:09.4452823Z         
2025-03-14T04:53:09.4453201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4453697Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:09.4453968Z         
2025-03-14T04:53:09.4454313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4455053Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4455594Z         
2025-03-14T04:53:09.4455954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4457758Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4459360Z         
2025-03-14T04:53:09.4459735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4460208Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:09.4460465Z         
2025-03-14T04:53:09.4460792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4461518Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4462059Z         
2025-03-14T04:53:09.4462408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4464364Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4466068Z         
2025-03-14T04:53:09.4466446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4466926Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:09.4467187Z         
2025-03-14T04:53:09.4467527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4468264Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4468802Z         
2025-03-14T04:53:09.4469149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4470970Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4472573Z         
2025-03-14T04:53:09.4472937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4473446Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:09.4473713Z         
2025-03-14T04:53:09.4474080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4474558Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:09.4474823Z         
2025-03-14T04:53:09.4475158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4475880Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4476412Z         
2025-03-14T04:53:09.4476817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4478657Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4480299Z         
2025-03-14T04:53:09.4480680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4481168Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:09.4481422Z         
2025-03-14T04:53:09.4481928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4482644Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4483173Z         
2025-03-14T04:53:09.4483520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4485321Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4486902Z         
2025-03-14T04:53:09.4487270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4487733Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:09.4487984Z         
2025-03-14T04:53:09.4488311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4489022Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4489549Z         
2025-03-14T04:53:09.4489893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4491780Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4493393Z         
2025-03-14T04:53:09.4493755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4494230Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:09.4494493Z         
2025-03-14T04:53:09.4494857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4495327Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:09.4495586Z         
2025-03-14T04:53:09.4495917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4496625Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4497144Z         
2025-03-14T04:53:09.4497488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4499408Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4501084Z         
2025-03-14T04:53:09.4501473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4501970Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:09.4502238Z         
2025-03-14T04:53:09.4502587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4503357Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4503927Z         
2025-03-14T04:53:09.4504434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4506410Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4508079Z         
2025-03-14T04:53:09.4508474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4508972Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:09.4509238Z         
2025-03-14T04:53:09.4509577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4510332Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4510882Z         
2025-03-14T04:53:09.4511241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4514094Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4515755Z         
2025-03-14T04:53:09.4516120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4516588Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:09.4516849Z         
2025-03-14T04:53:09.4517207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4517675Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:09.4517937Z         
2025-03-14T04:53:09.4518265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4518970Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4519551Z         
2025-03-14T04:53:09.4519899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4521679Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4523300Z         
2025-03-14T04:53:09.4523667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4524137Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:09.4524387Z         
2025-03-14T04:53:09.4524719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4525430Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4525960Z         
2025-03-14T04:53:09.4526305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4528073Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4529634Z         
2025-03-14T04:53:09.4529997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4530461Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:09.4530713Z         
2025-03-14T04:53:09.4531040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4531745Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4532266Z         
2025-03-14T04:53:09.4532640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4534425Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4536053Z         
2025-03-14T04:53:09.4536414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4536885Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:09.4537150Z         
2025-03-14T04:53:09.4537510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4537977Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:09.4538234Z         
2025-03-14T04:53:09.4538568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4539297Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4539828Z         
2025-03-14T04:53:09.4540199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4542138Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4543853Z         
2025-03-14T04:53:09.4544300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4544809Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:09.4545075Z         
2025-03-14T04:53:09.4545420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4546182Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4546793Z         
2025-03-14T04:53:09.4547167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4549068Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4550745Z         
2025-03-14T04:53:09.4551132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4551624Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:09.4551890Z         
2025-03-14T04:53:09.4552241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4552990Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4553548Z         
2025-03-14T04:53:09.4553916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4555811Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4557450Z         
2025-03-14T04:53:09.4557823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4558308Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:09.4558576Z         
2025-03-14T04:53:09.4558948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4559428Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:09.4559685Z         
2025-03-14T04:53:09.4560022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4560795Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4561320Z         
2025-03-14T04:53:09.4561655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4563537Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4565166Z         
2025-03-14T04:53:09.4565543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4566031Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:09.4566296Z         
2025-03-14T04:53:09.4566637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4567376Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4567918Z         
2025-03-14T04:53:09.4568271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4570112Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4571734Z         
2025-03-14T04:53:09.4572110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4572589Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:09.4572847Z         
2025-03-14T04:53:09.4573186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4573956Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4574497Z         
2025-03-14T04:53:09.4574848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4576703Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4578401Z         
2025-03-14T04:53:09.4578777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4579280Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:09.4579562Z         
2025-03-14T04:53:09.4579942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4580435Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:09.4580706Z         
2025-03-14T04:53:09.4581054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4581924Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4582484Z         
2025-03-14T04:53:09.4582848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4584768Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4586444Z         
2025-03-14T04:53:09.4586832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4587327Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:09.4587597Z         
2025-03-14T04:53:09.4587944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4588781Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4589339Z         
2025-03-14T04:53:09.4589703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4591671Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4593355Z         
2025-03-14T04:53:09.4593739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4594811Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:09.4595280Z         
2025-03-14T04:53:09.4595685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4596870Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4597679Z         
2025-03-14T04:53:09.4598115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4600486Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4602767Z         
2025-03-14T04:53:09.4603373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4604130Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:09.4604497Z         
2025-03-14T04:53:09.4605112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4605874Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:09.4606170Z         
2025-03-14T04:53:09.4606865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4608101Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4608944Z         
2025-03-14T04:53:09.4609363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4611638Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4646234Z         
2025-03-14T04:53:09.4647104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4647311Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:09.4647390Z         
2025-03-14T04:53:09.4647903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4648643Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4648752Z         
2025-03-14T04:53:09.4649091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4651364Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4651484Z         
2025-03-14T04:53:09.4652102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4652531Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:09.4652633Z         
2025-03-14T04:53:09.4652967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4653817Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4654103Z         
2025-03-14T04:53:09.4654515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4656758Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4656860Z         
2025-03-14T04:53:09.4657407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4657607Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:09.4657687Z         
2025-03-14T04:53:09.4658194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4658388Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:09.4658472Z         
2025-03-14T04:53:09.4658764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4659238Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4659503Z         
2025-03-14T04:53:09.4659864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4661692Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4661781Z         
2025-03-14T04:53:09.4662145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4662302Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:09.4662384Z         
2025-03-14T04:53:09.4662695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4663157Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4663261Z         
2025-03-14T04:53:09.4663567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4665663Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4665772Z         
2025-03-14T04:53:09.4666149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4666336Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:09.4666419Z         
2025-03-14T04:53:09.4666734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4667277Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4667354Z         
2025-03-14T04:53:09.4667646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4669249Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4669323Z         
2025-03-14T04:53:09.4669628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4669791Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:09.4669872Z         
2025-03-14T04:53:09.4670215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4670379Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:09.4670450Z         
2025-03-14T04:53:09.4670730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4671225Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4671307Z         
2025-03-14T04:53:09.4671590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4673447Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4673543Z         
2025-03-14T04:53:09.4673916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4674082Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:09.4674155Z         
2025-03-14T04:53:09.4674433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4674891Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4674972Z         
2025-03-14T04:53:09.4675264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4676868Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4676955Z         
2025-03-14T04:53:09.4677276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4677443Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:09.4677565Z         
2025-03-14T04:53:09.4677845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4678307Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4679055Z         
2025-03-14T04:53:09.4679353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4680999Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4681154Z         
2025-03-14T04:53:09.4695004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4695296Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:09.4695382Z         
2025-03-14T04:53:09.4695709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4695864Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:09.4695939Z         
2025-03-14T04:53:09.4696214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4696674Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4696768Z         
2025-03-14T04:53:09.4697036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4698651Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4698732Z         
2025-03-14T04:53:09.4699141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4699304Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:09.4699373Z         
2025-03-14T04:53:09.4699648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4700157Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4700235Z         
2025-03-14T04:53:09.4700523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4702156Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4702230Z         
2025-03-14T04:53:09.4702536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4702690Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:09.4702760Z         
2025-03-14T04:53:09.4703026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4703484Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4703560Z         
2025-03-14T04:53:09.4703871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4705685Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4705775Z         
2025-03-14T04:53:09.4706069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4706280Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:09.4706347Z         
2025-03-14T04:53:09.4706642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4706825Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:09.4706897Z         
2025-03-14T04:53:09.4707165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4707614Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4707682Z         
2025-03-14T04:53:09.4707963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4709578Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4709659Z         
2025-03-14T04:53:09.4709961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4710104Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:09.4710188Z         
2025-03-14T04:53:09.4710449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4710891Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4710956Z         
2025-03-14T04:53:09.4711241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4713470Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4713564Z         
2025-03-14T04:53:09.4713931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4714076Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:09.4714196Z         
2025-03-14T04:53:09.4714471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4714966Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4715038Z         
2025-03-14T04:53:09.4715318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4716919Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4716987Z         
2025-03-14T04:53:09.4717291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4717445Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:09.4717519Z         
2025-03-14T04:53:09.4717806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4717957Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:09.4718019Z         
2025-03-14T04:53:09.4718278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4718709Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4718773Z         
2025-03-14T04:53:09.4719045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4720646Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4720719Z         
2025-03-14T04:53:09.4721010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4721186Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:09.4721248Z         
2025-03-14T04:53:09.4721505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4721938Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4722005Z         
2025-03-14T04:53:09.4722282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4723851Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4723922Z         
2025-03-14T04:53:09.4724212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4724358Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:09.4724423Z         
2025-03-14T04:53:09.4724679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4725118Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4725184Z         
2025-03-14T04:53:09.4725473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4727043Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4727151Z         
2025-03-14T04:53:09.4727440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4727591Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:09.4727693Z         
2025-03-14T04:53:09.4727975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4728122Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:09.4728185Z         
2025-03-14T04:53:09.4728443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4728861Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4728937Z         
2025-03-14T04:53:09.4729201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4730741Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4730822Z         
2025-03-14T04:53:09.4731107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4731250Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:09.4731315Z         
2025-03-14T04:53:09.4731570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4731989Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4732064Z         
2025-03-14T04:53:09.4732322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4733920Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4733997Z         
2025-03-14T04:53:09.4734290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4734474Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:09.4734541Z         
2025-03-14T04:53:09.4734813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4735263Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4735340Z         
2025-03-14T04:53:09.4735623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4737163Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4737238Z         
2025-03-14T04:53:09.4737529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4737695Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:09.4737762Z         
2025-03-14T04:53:09.4738068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4738219Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:09.4738297Z         
2025-03-14T04:53:09.4738558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4739004Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4739079Z         
2025-03-14T04:53:09.4739381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4741151Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4741261Z         
2025-03-14T04:53:09.4741590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4741743Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:09.4741825Z         
2025-03-14T04:53:09.4742103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4742580Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4742657Z         
2025-03-14T04:53:09.4742966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4744741Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4744826Z         
2025-03-14T04:53:09.4745156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4745311Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:09.4745393Z         
2025-03-14T04:53:09.4745677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4746162Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4746237Z         
2025-03-14T04:53:09.4746669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4748601Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4748692Z         
2025-03-14T04:53:09.4749051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4749220Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:09.4749304Z         
2025-03-14T04:53:09.4749618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4749781Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:09.4749854Z         
2025-03-14T04:53:09.4750146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4750573Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4750651Z         
2025-03-14T04:53:09.4750913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4752436Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4752517Z         
2025-03-14T04:53:09.4752802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4752954Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:09.4753025Z         
2025-03-14T04:53:09.4753280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4753714Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4753781Z         
2025-03-14T04:53:09.4754051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4755590Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4755686Z         
2025-03-14T04:53:09.4755987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4756129Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:09.4756205Z         
2025-03-14T04:53:09.4756455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4756893Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4756964Z         
2025-03-14T04:53:09.4757238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4758781Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4758851Z         
2025-03-14T04:53:09.4759137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4759290Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:09.4759364Z         
2025-03-14T04:53:09.4759648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4759808Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:09.4759874Z         
2025-03-14T04:53:09.4760135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4760556Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4760634Z         
2025-03-14T04:53:09.4760907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4762472Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4762586Z         
2025-03-14T04:53:09.4762874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4763023Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:09.4763101Z         
2025-03-14T04:53:09.4763355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4764151Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4764231Z         
2025-03-14T04:53:09.4764532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4766317Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4766407Z         
2025-03-14T04:53:09.4766716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4766857Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:09.4766931Z         
2025-03-14T04:53:09.4767187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4767624Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4767693Z         
2025-03-14T04:53:09.4767970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4769540Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4769648Z         
2025-03-14T04:53:09.4769942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4770106Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:09.4770183Z         
2025-03-14T04:53:09.4770473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4770630Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:09.4770695Z         
2025-03-14T04:53:09.4770958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4771381Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4771456Z         
2025-03-14T04:53:09.4771722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4773249Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4773335Z         
2025-03-14T04:53:09.4773625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4773779Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:09.4773846Z         
2025-03-14T04:53:09.4774108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4774546Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4774622Z         
2025-03-14T04:53:09.4774886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4776452Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4776565Z         
2025-03-14T04:53:09.4776854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4777006Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:09.4777071Z         
2025-03-14T04:53:09.4777328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4777758Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4777831Z         
2025-03-14T04:53:09.4778091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4779624Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4779703Z         
2025-03-14T04:53:09.4779982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4780152Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:09.4780218Z         
2025-03-14T04:53:09.4780512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4780662Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:09.4780742Z         
2025-03-14T04:53:09.4781004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4781616Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4781694Z         
2025-03-14T04:53:09.4782089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4783797Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4783929Z         
2025-03-14T04:53:09.4784347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4784509Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:09.4784592Z         
2025-03-14T04:53:09.4784873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4785336Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4785407Z         
2025-03-14T04:53:09.4785695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4787306Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4787380Z         
2025-03-14T04:53:09.4787695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4787841Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:09.4787917Z         
2025-03-14T04:53:09.4788182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4788663Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4788738Z         
2025-03-14T04:53:09.4789013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4790667Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4790777Z         
2025-03-14T04:53:09.4791081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4791252Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:09.4791330Z         
2025-03-14T04:53:09.4791629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4791794Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:09.4791869Z         
2025-03-14T04:53:09.4792130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4792579Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.4792648Z         
2025-03-14T04:53:09.4792932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4794520Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4794598Z         
2025-03-14T04:53:09.4794907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4795054Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:09.4795131Z         
2025-03-14T04:53:09.4795396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4795849Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.4795918Z         
2025-03-14T04:53:09.4796234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4797802Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4797912Z         
2025-03-14T04:53:09.4798223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4798368Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:09.4798446Z         
2025-03-14T04:53:09.4798710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4799174Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.4799237Z         
2025-03-14T04:53:09.4799516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.4801042Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.4801116Z         
2025-03-14T04:53:09.4801407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.4801566Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:09.4801640Z         
2025-03-14T04:53:09.4801927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.4802079Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:09.4802144Z         
2025-03-14T04:53:09.4802591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.4802790Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.4802862Z         
2025-03-14T04:53:09.4803161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.4803339Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:09.4803404Z         
2025-03-14T04:53:09.4803846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.4804000Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.4804074Z         
2025-03-14T04:53:09.4804368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.4804516Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:09.4804583Z         
2025-03-14T04:53:09.4804961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:09.4805145Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:09.4805257Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:09.4805380Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:09.4805454Z         
2025-03-14T04:53:09.4805782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:09.4805921Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:09.4805988Z         
2025-03-14T04:53:09.4806322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:09.4806441Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:09.4806519Z         
2025-03-14T04:53:09.4806892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:09.4807117Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:09.4807183Z         
2025-03-14T04:53:09.4807639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:09.4807775Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:09.4808202Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:09.4808340Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:09.4808457Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:09.4808530Z         
2025-03-14T04:53:09.4808830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.4808967Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:53:09.4809070Z         
2025-03-14T04:53:09.4809339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.4810121Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:09.4810226Z         
2025-03-14T04:53:09.4810502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:09.4810702Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:53:09.4810768Z         
2025-03-14T04:53:09.4811159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:09.4812034Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:09.4812099Z         
2025-03-14T04:53:09.4812470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:09.4813292Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:09.4813368Z         
2025-03-14T04:53:09.4813713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:09.4813878Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:09.4814021Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:09.4814094Z         
2025-03-14T04:53:09.4814512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:09.4814680Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:53:09.4814855Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:09.4815042Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:09.4815114Z         
2025-03-14T04:53:09.4815513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:09.4815761Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:09.4815828Z         
2025-03-14T04:53:09.4816265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:09.4816460Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:09.4816617Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:09.4816758Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:09.4816832Z         
2025-03-14T04:53:09.4817206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:09.4817382Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:09.4817448Z         
2025-03-14T04:53:09.4817771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:09.4817916Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:09.4817994Z         
2025-03-14T04:53:09.4818308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:09.4818447Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:09.4818573Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.4818728Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:09.4818793Z         
2025-03-14T04:53:09.4819117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:09.4819243Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:09.4819370Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:09.4819515Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:09.4819590Z         
2025-03-14T04:53:09.4819900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:09.4820032Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.4820125Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:09.4820255Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:09.4820321Z         
2025-03-14T04:53:09.4820638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:09.4820783Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:09.4820885Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:09.4821014Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:09.4821088Z         
2025-03-14T04:53:09.4821443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:09.4821648Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:09.4821769Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:09.4821844Z         
2025-03-14T04:53:09.4822147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:09.4822337Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:09.4822459Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:09.4822525Z         
2025-03-14T04:53:09.4822830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:09.4822983Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:09.4823109Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:09.4823177Z         
2025-03-14T04:53:09.4823485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:09.4823666Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:09.4823789Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:09.4823856Z         
2025-03-14T04:53:09.4824298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:09.4824453Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:09.4824531Z         
2025-03-14T04:53:09.4824891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:09.4825052Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:09.4825119Z         
2025-03-14T04:53:09.4825490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:09.4825646Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:09.4825779Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:09.4825934Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:09.4826079Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:09.4826145Z         
2025-03-14T04:53:09.4826500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:09.4826640Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:09.4826774Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:09.4826930Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:09.4827077Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:09.4827145Z         
2025-03-14T04:53:09.4827497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:09.4827615Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:09.4827826Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:09.4827959Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:09.4828034Z         
2025-03-14T04:53:09.4828360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:09.4828521Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:09.4828686Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:09.4828826Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:09.4828891Z         
2025-03-14T04:53:09.4829211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:09.4829309Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:09.4829434Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:09.4829499Z         
2025-03-14T04:53:09.4829810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:09.4829917Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:09.4830031Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:09.4830097Z         
2025-03-14T04:53:09.4830405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:09.4830530Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:09.4830660Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:09.4830732Z         
2025-03-14T04:53:09.4831029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:09.4831147Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:09.4831271Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:09.4831344Z         
2025-03-14T04:53:09.4831690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:09.4831877Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:09.4831942Z         
2025-03-14T04:53:09.4832280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:09.4832440Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:09.4832512Z         
2025-03-14T04:53:09.4832892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:09.4833072Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:09.4833137Z         
2025-03-14T04:53:09.4833621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:09.4833794Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:09.4833867Z         
2025-03-14T04:53:09.4834161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.4834342Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:09.4834407Z         
2025-03-14T04:53:09.4834841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:09.4834953Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:09.4835064Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:09.4835180Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:09.4835254Z         
2025-03-14T04:53:09.4835711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:09.4835885Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:09.4836124Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:09.4836198Z         
2025-03-14T04:53:09.4836653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:09.4836825Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:09.4836889Z         
2025-03-14T04:53:09.4837193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.4837356Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:09.4837424Z         
2025-03-14T04:53:09.4837807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:09.4837953Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:09.4838025Z         
2025-03-14T04:53:09.4838327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.4838482Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:09.4838548Z         
2025-03-14T04:53:09.4838925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:09.4839068Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:09.4839142Z         
2025-03-14T04:53:09.4839622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:09.4839767Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:09.4839887Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:09.4840124Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:09.4840260Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:09.4840334Z         
2025-03-14T04:53:09.4840698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:09.4840856Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:09.4840922Z         
2025-03-14T04:53:09.4841469Z 
2025-03-14T04:53:09.4841573Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:09.4931527Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", 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1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:09.4932280Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:09.4932592Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4932978Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4933379Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4933763Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4934129Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4934482Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4934822Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4935154Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4935474Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4935780Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4936067Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4936404Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4936740Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4937049Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4937361Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4937644Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4938029Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4938417Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4938788Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4939205Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4939574Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.4940042Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4940444Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4940863Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.4941272Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.4941588Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4941994Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4942383Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4942761Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4943101Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4943433Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4943814Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4944237Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4944602Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4944940Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4945270Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4945657Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4946036Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4946444Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4946812Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4947188Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4947566Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4947940Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4948291Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4948641Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4948951Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4949327Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4949690Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4950055Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4950392Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4950706Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4951082Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4951454Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4951811Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4952148Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4952465Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4952833Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4953206Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4953594Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4953939Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4954286Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4954664Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4955032Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4955381Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4955726Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4956036Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4956414Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4956787Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4957143Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4957487Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4957816Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.4958209Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4958604Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4958973Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.4959325Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.4959641Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4960005Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4960412Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4960770Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4961710Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4962025Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4962393Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4962731Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4963038Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4963350Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4963626Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4963979Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4964315Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4964624Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4964945Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4965251Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4965623Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4965989Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4966344Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4966681Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4966994Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4967405Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4967771Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4968131Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4968505Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4968826Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4969205Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4969581Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4969934Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4970287Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4970607Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4970981Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4971358Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4971711Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4972062Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4972375Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4972758Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4973121Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4973479Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4973832Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4974143Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4974568Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4974943Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4975295Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4975606Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4975897Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4976236Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4976576Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4976901Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4977211Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4977503Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4977860Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4978209Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4978537Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4978862Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4979149Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4979517Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4979868Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4980187Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4980501Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4980830Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.4981193Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4981747Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4982112Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.4982464Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.4982772Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4983138Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4983472Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4983799Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4984149Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4984472Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4984831Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4985192Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4985538Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4985859Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4986154Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4986492Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4986835Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4987160Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4987492Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4987849Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4988206Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4988609Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4988937Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4989270Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4989564Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4989922Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4990273Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4990611Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4990934Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4991234Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4991591Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4991938Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4992272Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4992594Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4993011Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4993370Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4993726Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4994054Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4994416Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4994715Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.4995108Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4995459Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4995786Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.4996115Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.4996406Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.4996768Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4997122Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4997456Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.4997783Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.4998076Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:09.4998432Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.4998783Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.4999118Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.4999439Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.4999748Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5000081Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5000417Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5000743Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5001083Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5001375Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5001746Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5002082Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5002393Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5002703Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5002981Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5003320Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5003654Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5003964Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5004272Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5004553Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5004888Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5005223Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5005551Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5005860Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5006152Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5006499Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5006839Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5007191Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5007502Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5007821Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5008161Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5008502Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5008824Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5009138Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5009432Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5009771Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5010117Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5010436Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5010754Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5011037Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5011383Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5011714Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5012041Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5012362Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5012648Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5012996Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5013358Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5013688Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5013996Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5014314Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5014650Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5014995Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5015320Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5015634Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5015926Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5016261Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5016607Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5016920Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5017236Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5017519Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5017862Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5018206Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5018523Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5018839Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5019120Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5019469Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5019832Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5020160Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5020516Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5020812Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5021166Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5021510Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5021837Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5022152Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5022450Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5022791Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5023136Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5023468Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5023789Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5024159Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5024541Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5024903Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5025236Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5025569Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5025853Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5026238Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5026576Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5026932Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5027248Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5027542Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5027898Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5028240Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5028572Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5028889Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5029183Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5029528Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5029874Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5030203Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5030516Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5030813Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5031158Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5031503Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5031825Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5032146Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5032439Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5032817Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5033153Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5033496Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5033816Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5034103Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5034456Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5034800Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5035123Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5035428Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5035716Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5036056Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5036388Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5036707Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5037007Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5037297Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5037629Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5037964Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5038277Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5038590Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5038910Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5039248Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5039612Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5039929Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5040249Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5040543Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5040885Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5041210Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5041532Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5041837Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5042117Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5042461Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5042790Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5043106Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5043407Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5043692Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5044023Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5044358Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5044675Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5045020Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5045307Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5045676Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5046016Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5046332Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5046649Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5046933Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5047284Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5047625Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5047939Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5048256Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5048536Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5048884Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5049216Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5049543Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5049855Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5050146Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5050492Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5050829Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5051183Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5051488Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5051774Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5052173Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5052522Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5052847Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5053168Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5053469Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5053820Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5054159Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5054493Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5054815Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5055100Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5055450Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5055798Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5056137Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5056458Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5056747Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5057107Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5057455Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5057817Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5058128Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5058460Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5058798Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5059144Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5059472Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5059780Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5060072Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5060411Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5060758Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5061080Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5061403Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5061690Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5062040Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5062389Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5062706Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5063026Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5063311Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5063657Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5064050Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5064462Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5064829Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5065141Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5065515Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5065857Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5066188Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5066503Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5066800Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5067142Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5067493Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5067812Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5068131Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5068423Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5068763Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5069110Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5069429Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5069749Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5070032Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5070421Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5070766Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5071129Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5071446Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5071731Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5072079Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5072442Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5072790Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5073096Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5073388Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5073733Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5074078Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5074409Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5074717Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5075008Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5075350Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5075695Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5076016Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5076333Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5076626Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5077004Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5077355Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5077704Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5078025Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5078315Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5078662Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5078996Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5079327Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5079644Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5079932Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5080279Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5080615Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5080948Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5081274Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5081739Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5082090Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5082457Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5082820Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5083151Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5083535Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5083903Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5084315Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5084654Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5084987Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5085291Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5085659Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5086022Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5086373Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5086709Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5087013Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5087386Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5087742Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5088089Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5088419Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5088724Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5089091Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5089448Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5089794Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5090155Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5090464Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5090868Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5091235Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5091573Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5091919Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5092305Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:09.5092639Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:09.5092971Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:09.5093356Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:09.5093740Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:09.5094108Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:09.5094476Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:09.5094553Z         
2025-03-14T04:53:09.5094858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5095350Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5095430Z         
2025-03-14T04:53:09.5095724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5097273Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5097353Z         
2025-03-14T04:53:09.5097649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:09.5097835Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:09.5097903Z         
2025-03-14T04:53:09.5098282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:09.5098528Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:09.5098605Z         
2025-03-14T04:53:09.5098873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5099315Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5099391Z         
2025-03-14T04:53:09.5099659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5101196Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5101265Z         
2025-03-14T04:53:09.5101568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5101714Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:09.5101779Z         
2025-03-14T04:53:09.5102033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5102471Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5102547Z         
2025-03-14T04:53:09.5102812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5104467Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5104577Z         
2025-03-14T04:53:09.5104892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5105049Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:09.5105119Z         
2025-03-14T04:53:09.5105397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5105834Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5105912Z         
2025-03-14T04:53:09.5106178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5107757Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5107836Z         
2025-03-14T04:53:09.5108094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5108546Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5108613Z         
2025-03-14T04:53:09.5108889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5110480Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5110558Z         
2025-03-14T04:53:09.5110890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5111047Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:09.5111150Z         
2025-03-14T04:53:09.5111443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5111606Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:09.5111673Z         
2025-03-14T04:53:09.5111939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5112375Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5112449Z         
2025-03-14T04:53:09.5112723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5114309Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5114385Z         
2025-03-14T04:53:09.5114678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5114836Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:09.5114903Z         
2025-03-14T04:53:09.5115165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5115603Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5115676Z         
2025-03-14T04:53:09.5115947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5117552Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5117630Z         
2025-03-14T04:53:09.5117924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5118119Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:09.5118189Z         
2025-03-14T04:53:09.5118452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5118891Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5118966Z         
2025-03-14T04:53:09.5119240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5120827Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5120905Z         
2025-03-14T04:53:09.5121193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5121356Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:09.5121439Z         
2025-03-14T04:53:09.5121729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5121877Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:09.5121949Z         
2025-03-14T04:53:09.5122197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5122625Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5122690Z         
2025-03-14T04:53:09.5122963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5124522Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5124620Z         
2025-03-14T04:53:09.5124917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5125053Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:09.5125125Z         
2025-03-14T04:53:09.5125372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5125800Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5125866Z         
2025-03-14T04:53:09.5126135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5127655Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5127722Z         
2025-03-14T04:53:09.5128016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5128151Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:09.5128224Z         
2025-03-14T04:53:09.5128470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5128900Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5128965Z         
2025-03-14T04:53:09.5129237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5130787Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5130854Z         
2025-03-14T04:53:09.5131138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5131322Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:09.5131395Z         
2025-03-14T04:53:09.5131678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5131839Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:09.5131903Z         
2025-03-14T04:53:09.5132163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5132587Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5132663Z         
2025-03-14T04:53:09.5132924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5134459Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5134534Z         
2025-03-14T04:53:09.5134819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5134972Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:09.5135038Z         
2025-03-14T04:53:09.5135294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5135723Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5135797Z         
2025-03-14T04:53:09.5136060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5137613Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5137717Z         
2025-03-14T04:53:09.5138001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5138154Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:09.5138218Z         
2025-03-14T04:53:09.5138477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5138960Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5139035Z         
2025-03-14T04:53:09.5139340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5140926Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5141004Z         
2025-03-14T04:53:09.5141280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5141784Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5141857Z         
2025-03-14T04:53:09.5142145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5143826Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5143898Z         
2025-03-14T04:53:09.5144313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5144480Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:09.5144555Z         
2025-03-14T04:53:09.5144907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5145075Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:09.5145141Z         
2025-03-14T04:53:09.5145425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5145886Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5145955Z         
2025-03-14T04:53:09.5146255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5147868Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5147945Z         
2025-03-14T04:53:09.5148267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5148430Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:09.5148510Z         
2025-03-14T04:53:09.5148788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5149290Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5149361Z         
2025-03-14T04:53:09.5149646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5151299Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5151381Z         
2025-03-14T04:53:09.5151690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5151878Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:09.5151955Z         
2025-03-14T04:53:09.5152223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5152693Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5152762Z         
2025-03-14T04:53:09.5153054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5154612Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5154688Z         
2025-03-14T04:53:09.5154978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5155133Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:09.5155206Z         
2025-03-14T04:53:09.5155491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5155648Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:09.5155712Z         
2025-03-14T04:53:09.5155969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5156423Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5156501Z         
2025-03-14T04:53:09.5156770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5158403Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5158509Z         
2025-03-14T04:53:09.5158820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5158971Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:09.5159037Z         
2025-03-14T04:53:09.5159303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5159765Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5159840Z         
2025-03-14T04:53:09.5160115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5161716Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5161790Z         
2025-03-14T04:53:09.5162091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5162246Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:09.5162312Z         
2025-03-14T04:53:09.5162577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5163018Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5163092Z         
2025-03-14T04:53:09.5163367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5164993Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5165071Z         
2025-03-14T04:53:09.5165358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5165561Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:09.5165629Z         
2025-03-14T04:53:09.5165925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5166078Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:09.5166152Z         
2025-03-14T04:53:09.5166413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5166851Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5166921Z         
2025-03-14T04:53:09.5167198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5168792Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5168861Z         
2025-03-14T04:53:09.5169157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5169302Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:09.5169375Z         
2025-03-14T04:53:09.5169635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5170078Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5170146Z         
2025-03-14T04:53:09.5170424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5172030Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5172124Z         
2025-03-14T04:53:09.5172423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5172568Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:09.5172642Z         
2025-03-14T04:53:09.5172895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5173351Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5173417Z         
2025-03-14T04:53:09.5173696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5175287Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5175356Z         
2025-03-14T04:53:09.5175649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5175807Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:09.5175882Z         
2025-03-14T04:53:09.5176170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5176329Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:09.5176395Z         
2025-03-14T04:53:09.5176661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5177091Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5177170Z         
2025-03-14T04:53:09.5177443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5179067Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5179174Z         
2025-03-14T04:53:09.5179466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5179614Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:09.5179680Z         
2025-03-14T04:53:09.5179944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5180371Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5180451Z         
2025-03-14T04:53:09.5180732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5182437Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5182524Z         
2025-03-14T04:53:09.5182812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5182959Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:09.5183026Z         
2025-03-14T04:53:09.5183288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5183720Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5183799Z         
2025-03-14T04:53:09.5184111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5185756Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5185877Z         
2025-03-14T04:53:09.5186137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5186589Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5186669Z         
2025-03-14T04:53:09.5186944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5188599Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5188675Z         
2025-03-14T04:53:09.5188981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5189134Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:09.5189217Z         
2025-03-14T04:53:09.5189525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5189681Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:09.5189750Z         
2025-03-14T04:53:09.5190013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5190454Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5190523Z         
2025-03-14T04:53:09.5190805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5192407Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5192484Z         
2025-03-14T04:53:09.5192785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5192962Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:09.5193039Z         
2025-03-14T04:53:09.5193297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5193728Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5193794Z         
2025-03-14T04:53:09.5194070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5195625Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5195701Z         
2025-03-14T04:53:09.5195995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5196133Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:09.5196206Z         
2025-03-14T04:53:09.5196458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5196894Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5196961Z         
2025-03-14T04:53:09.5197241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5198803Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5198884Z         
2025-03-14T04:53:09.5199185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5199328Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:09.5199431Z         
2025-03-14T04:53:09.5199720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5199870Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:09.5199935Z         
2025-03-14T04:53:09.5200192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5200606Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5200678Z         
2025-03-14T04:53:09.5200947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5202464Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5202541Z         
2025-03-14T04:53:09.5202824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5202964Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:09.5203028Z         
2025-03-14T04:53:09.5203284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5203700Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5203774Z         
2025-03-14T04:53:09.5204043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5205592Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5205668Z         
2025-03-14T04:53:09.5205958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5206133Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:09.5206198Z         
2025-03-14T04:53:09.5206458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5206875Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5206950Z         
2025-03-14T04:53:09.5207214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5208725Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5208800Z         
2025-03-14T04:53:09.5209080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5209239Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:09.5209305Z         
2025-03-14T04:53:09.5209592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5209732Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:09.5209805Z         
2025-03-14T04:53:09.5210054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5210474Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5210542Z         
2025-03-14T04:53:09.5210810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5212350Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5212447Z         
2025-03-14T04:53:09.5212739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5212873Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:09.5212945Z         
2025-03-14T04:53:09.5213192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5213616Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5213682Z         
2025-03-14T04:53:09.5213958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5215499Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5215567Z         
2025-03-14T04:53:09.5215858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5215991Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:09.5216063Z         
2025-03-14T04:53:09.5216311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5216739Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5216804Z         
2025-03-14T04:53:09.5217076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5218646Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5218713Z         
2025-03-14T04:53:09.5219004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5219187Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:09.5219263Z         
2025-03-14T04:53:09.5219545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5219694Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:09.5219759Z         
2025-03-14T04:53:09.5220018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5220422Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5220499Z         
2025-03-14T04:53:09.5220762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5222260Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5222336Z         
2025-03-14T04:53:09.5222622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5222761Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:09.5222827Z         
2025-03-14T04:53:09.5223084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5223505Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5223580Z         
2025-03-14T04:53:09.5223849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5225560Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5225669Z         
2025-03-14T04:53:09.5225955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5226096Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:09.5226164Z         
2025-03-14T04:53:09.5226443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5226925Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5227000Z         
2025-03-14T04:53:09.5227279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5228879Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5228960Z         
2025-03-14T04:53:09.5229263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5229423Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:09.5229491Z         
2025-03-14T04:53:09.5229831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5229979Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:09.5230057Z         
2025-03-14T04:53:09.5230328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5230804Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5230881Z         
2025-03-14T04:53:09.5231172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5232810Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5232909Z         
2025-03-14T04:53:09.5233218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5233359Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:09.5233435Z         
2025-03-14T04:53:09.5233702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5234160Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5234239Z         
2025-03-14T04:53:09.5234516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5236137Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5236210Z         
2025-03-14T04:53:09.5236521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5236670Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:09.5236743Z         
2025-03-14T04:53:09.5237000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5237463Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5237541Z         
2025-03-14T04:53:09.5237811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5239402Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5239501Z         
2025-03-14T04:53:09.5239804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5239962Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:09.5240029Z         
2025-03-14T04:53:09.5240339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5240485Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:09.5240560Z         
2025-03-14T04:53:09.5240822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5241265Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5241334Z         
2025-03-14T04:53:09.5241615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5243182Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5243259Z         
2025-03-14T04:53:09.5243559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5243696Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:09.5243768Z         
2025-03-14T04:53:09.5244029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5244464Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5244531Z         
2025-03-14T04:53:09.5244812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5246425Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5246531Z         
2025-03-14T04:53:09.5246833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5246971Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:09.5247047Z         
2025-03-14T04:53:09.5247306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5247741Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5247813Z         
2025-03-14T04:53:09.5248090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5249666Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5249746Z         
2025-03-14T04:53:09.5250051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5250194Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:09.5250266Z         
2025-03-14T04:53:09.5250548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5250697Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:09.5250763Z         
2025-03-14T04:53:09.5251019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5251431Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5251501Z         
2025-03-14T04:53:09.5251762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5253285Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5253392Z         
2025-03-14T04:53:09.5253674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5253819Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:09.5253885Z         
2025-03-14T04:53:09.5254143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5254557Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5254629Z         
2025-03-14T04:53:09.5254892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5256417Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5256491Z         
2025-03-14T04:53:09.5256776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5256917Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:09.5256982Z         
2025-03-14T04:53:09.5257244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5257671Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5257744Z         
2025-03-14T04:53:09.5258008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5259553Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5259663Z         
2025-03-14T04:53:09.5259941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5260091Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:09.5260154Z         
2025-03-14T04:53:09.5260445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5260586Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:09.5260658Z         
2025-03-14T04:53:09.5260907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5261324Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5261389Z         
2025-03-14T04:53:09.5261658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5263179Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5263248Z         
2025-03-14T04:53:09.5263542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5263685Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:09.5263760Z         
2025-03-14T04:53:09.5264023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5264545Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5264620Z         
2025-03-14T04:53:09.5264904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5266589Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5266690Z         
2025-03-14T04:53:09.5267004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5267158Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:09.5267235Z         
2025-03-14T04:53:09.5267499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5267955Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5268025Z         
2025-03-14T04:53:09.5268312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5269969Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5270041Z         
2025-03-14T04:53:09.5270348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5270512Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:09.5270594Z         
2025-03-14T04:53:09.5270892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5271048Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:09.5271119Z         
2025-03-14T04:53:09.5271391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5271828Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5271904Z         
2025-03-14T04:53:09.5272226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5273819Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5273933Z         
2025-03-14T04:53:09.5274235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5274390Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:09.5274459Z         
2025-03-14T04:53:09.5274731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5275163Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5275241Z         
2025-03-14T04:53:09.5275524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5277106Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5277187Z         
2025-03-14T04:53:09.5277478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5277624Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:09.5277692Z         
2025-03-14T04:53:09.5277954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5278386Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5278461Z         
2025-03-14T04:53:09.5278735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5280313Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5280418Z         
2025-03-14T04:53:09.5280707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5280876Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:09.5280943Z         
2025-03-14T04:53:09.5281243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5281390Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:09.5281570Z         
2025-03-14T04:53:09.5281834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5282268Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5282344Z         
2025-03-14T04:53:09.5282622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5284201Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5284273Z         
2025-03-14T04:53:09.5284570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5284717Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:09.5284787Z         
2025-03-14T04:53:09.5285053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5285483Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5285557Z         
2025-03-14T04:53:09.5285919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5287495Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5287622Z         
2025-03-14T04:53:09.5287918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5288068Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:09.5288138Z         
2025-03-14T04:53:09.5288401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5288836Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5288913Z         
2025-03-14T04:53:09.5289184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5290751Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5290828Z         
2025-03-14T04:53:09.5291115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5291281Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:09.5291348Z         
2025-03-14T04:53:09.5291647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5291790Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:09.5291865Z         
2025-03-14T04:53:09.5292122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5292586Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5292654Z         
2025-03-14T04:53:09.5292933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5294509Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5294587Z         
2025-03-14T04:53:09.5294892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5295035Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:09.5295111Z         
2025-03-14T04:53:09.5295369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5295808Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5295878Z         
2025-03-14T04:53:09.5296157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5297738Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5297817Z         
2025-03-14T04:53:09.5298122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5298264Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:09.5298338Z         
2025-03-14T04:53:09.5298592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5299033Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5299100Z         
2025-03-14T04:53:09.5299424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5301007Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5301108Z         
2025-03-14T04:53:09.5301402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5301554Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:09.5301631Z         
2025-03-14T04:53:09.5301920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5302070Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:09.5302136Z         
2025-03-14T04:53:09.5302395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5302823Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5302901Z         
2025-03-14T04:53:09.5303169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5304897Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5304985Z         
2025-03-14T04:53:09.5305307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5305456Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:09.5305522Z         
2025-03-14T04:53:09.5305787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5306290Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5306373Z         
2025-03-14T04:53:09.5306669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5308425Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5308509Z         
2025-03-14T04:53:09.5308832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5308993Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:09.5309068Z         
2025-03-14T04:53:09.5309352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5309828Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5309909Z         
2025-03-14T04:53:09.5310201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5311908Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5311990Z         
2025-03-14T04:53:09.5312295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5312460Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:09.5312524Z         
2025-03-14T04:53:09.5312811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5312952Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:09.5313025Z         
2025-03-14T04:53:09.5313304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5313722Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5313825Z         
2025-03-14T04:53:09.5314086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5315616Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5315685Z         
2025-03-14T04:53:09.5315972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5316106Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:09.5316178Z         
2025-03-14T04:53:09.5316425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5316859Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5316935Z         
2025-03-14T04:53:09.5317196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5318750Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5318819Z         
2025-03-14T04:53:09.5319118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5319255Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:09.5319328Z         
2025-03-14T04:53:09.5319583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5320057Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5320135Z         
2025-03-14T04:53:09.5320405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5322022Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5322092Z         
2025-03-14T04:53:09.5322385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5322546Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:09.5322612Z         
2025-03-14T04:53:09.5322907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5323052Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:09.5323129Z         
2025-03-14T04:53:09.5323385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5323816Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5323885Z         
2025-03-14T04:53:09.5324161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5325691Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5325765Z         
2025-03-14T04:53:09.5326049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5326181Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:09.5326252Z         
2025-03-14T04:53:09.5326527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5326951Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5327047Z         
2025-03-14T04:53:09.5327318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5328817Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5328894Z         
2025-03-14T04:53:09.5329182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5329326Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:09.5329395Z         
2025-03-14T04:53:09.5329638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5330058Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5330123Z         
2025-03-14T04:53:09.5330385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5331840Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5331914Z         
2025-03-14T04:53:09.5332188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5332332Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:09.5332402Z         
2025-03-14T04:53:09.5332676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5332855Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:09.5332922Z         
2025-03-14T04:53:09.5333180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5333651Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5333725Z         
2025-03-14T04:53:09.5333986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5335528Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5335604Z         
2025-03-14T04:53:09.5335887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5336032Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:09.5336097Z         
2025-03-14T04:53:09.5336350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5336769Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5336844Z         
2025-03-14T04:53:09.5337104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5338621Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5338696Z         
2025-03-14T04:53:09.5338980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5339121Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:09.5339186Z         
2025-03-14T04:53:09.5339473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5339892Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5339995Z         
2025-03-14T04:53:09.5340253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5341767Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5341841Z         
2025-03-14T04:53:09.5342119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5342273Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:09.5342338Z         
2025-03-14T04:53:09.5342627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5342766Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:09.5342838Z         
2025-03-14T04:53:09.5343089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5343508Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5343573Z         
2025-03-14T04:53:09.5343843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5345450Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5345525Z         
2025-03-14T04:53:09.5345855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5345992Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:09.5346068Z         
2025-03-14T04:53:09.5346317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5346777Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5346842Z         
2025-03-14T04:53:09.5347114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5348637Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5348709Z         
2025-03-14T04:53:09.5349003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5349139Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:09.5349218Z         
2025-03-14T04:53:09.5349464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5349896Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5349962Z         
2025-03-14T04:53:09.5350230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5351750Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5351821Z         
2025-03-14T04:53:09.5352107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5352285Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:09.5352360Z         
2025-03-14T04:53:09.5352643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5352790Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:09.5352882Z         
2025-03-14T04:53:09.5353142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5353564Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5353631Z         
2025-03-14T04:53:09.5353908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5355425Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5355501Z         
2025-03-14T04:53:09.5355786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5355929Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:09.5355996Z         
2025-03-14T04:53:09.5356254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5356679Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5356743Z         
2025-03-14T04:53:09.5357014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5358528Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5358605Z         
2025-03-14T04:53:09.5358922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5359066Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:09.5359139Z         
2025-03-14T04:53:09.5359387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5359853Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5359918Z         
2025-03-14T04:53:09.5360187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5361732Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5361810Z         
2025-03-14T04:53:09.5362109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5362257Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:09.5362331Z         
2025-03-14T04:53:09.5362625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5362778Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:09.5362843Z         
2025-03-14T04:53:09.5363100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5363515Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5363591Z         
2025-03-14T04:53:09.5363855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5365409Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5365482Z         
2025-03-14T04:53:09.5365759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5365966Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:09.5366029Z         
2025-03-14T04:53:09.5366304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5366722Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5366794Z         
2025-03-14T04:53:09.5367055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5368547Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5368620Z         
2025-03-14T04:53:09.5368909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5369058Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:09.5369126Z         
2025-03-14T04:53:09.5369387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5369814Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5369885Z         
2025-03-14T04:53:09.5370153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5371656Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5371730Z         
2025-03-14T04:53:09.5372048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5372209Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:09.5372304Z         
2025-03-14T04:53:09.5372591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5372736Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:09.5372810Z         
2025-03-14T04:53:09.5373059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5373483Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5373547Z         
2025-03-14T04:53:09.5373817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5375340Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5375406Z         
2025-03-14T04:53:09.5375695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5375837Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:09.5375909Z         
2025-03-14T04:53:09.5376156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5376592Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5376660Z         
2025-03-14T04:53:09.5376934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5378501Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5378569Z         
2025-03-14T04:53:09.5378863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5379034Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:09.5379108Z         
2025-03-14T04:53:09.5379357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5379788Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5379856Z         
2025-03-14T04:53:09.5380127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5381800Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5381880Z         
2025-03-14T04:53:09.5382185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5382358Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:09.5382438Z         
2025-03-14T04:53:09.5382740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5382902Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:09.5382972Z         
2025-03-14T04:53:09.5383249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5383696Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5383778Z         
2025-03-14T04:53:09.5384147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5385832Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5385959Z         
2025-03-14T04:53:09.5386250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5386400Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:09.5386469Z         
2025-03-14T04:53:09.5386733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5387170Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5387246Z         
2025-03-14T04:53:09.5387521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5389117Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5389198Z         
2025-03-14T04:53:09.5389496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5389649Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:09.5389719Z         
2025-03-14T04:53:09.5389996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5390433Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5390508Z         
2025-03-14T04:53:09.5390782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5392394Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5392473Z         
2025-03-14T04:53:09.5392797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5392968Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:09.5393036Z         
2025-03-14T04:53:09.5393329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5393483Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:09.5393550Z         
2025-03-14T04:53:09.5393807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5394239Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5394316Z         
2025-03-14T04:53:09.5394584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5396170Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5396248Z         
2025-03-14T04:53:09.5396536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5396682Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:09.5396748Z         
2025-03-14T04:53:09.5397014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5397449Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5397525Z         
2025-03-14T04:53:09.5397804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5399339Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5399442Z         
2025-03-14T04:53:09.5399727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5399871Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:09.5399936Z         
2025-03-14T04:53:09.5400188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5400616Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5400691Z         
2025-03-14T04:53:09.5400953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5402485Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5402563Z         
2025-03-14T04:53:09.5402841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5403004Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:09.5403069Z         
2025-03-14T04:53:09.5403353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5403500Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:09.5403575Z         
2025-03-14T04:53:09.5403823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5404251Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5404316Z         
2025-03-14T04:53:09.5404584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5406141Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5406245Z         
2025-03-14T04:53:09.5406536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5406675Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:09.5406753Z         
2025-03-14T04:53:09.5407003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5407435Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5407503Z         
2025-03-14T04:53:09.5407776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5409306Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5409382Z         
2025-03-14T04:53:09.5409672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5409810Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:09.5409880Z         
2025-03-14T04:53:09.5410133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5410586Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5410652Z         
2025-03-14T04:53:09.5410922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5412477Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5412577Z         
2025-03-14T04:53:09.5412864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5413020Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:09.5413094Z         
2025-03-14T04:53:09.5413374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5413525Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:09.5413589Z         
2025-03-14T04:53:09.5414035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.5414195Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.5414270Z         
2025-03-14T04:53:09.5414567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.5414716Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:09.5414782Z         
2025-03-14T04:53:09.5415226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.5415380Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.5415456Z         
2025-03-14T04:53:09.5415750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.5415899Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:09.5415964Z         
2025-03-14T04:53:09.5416348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:09.5416532Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:09.5416639Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:09.5416761Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:09.5416834Z         
2025-03-14T04:53:09.5417164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:09.5417300Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:09.5417373Z         
2025-03-14T04:53:09.5417699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:09.5417825Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:09.5417891Z         
2025-03-14T04:53:09.5418305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:09.5418520Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:09.5418637Z         
2025-03-14T04:53:09.5419059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:09.5419194Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:09.5419620Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:09.5419752Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:09.5419867Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:09.5419940Z         
2025-03-14T04:53:09.5420240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.5420377Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:53:09.5420442Z         
2025-03-14T04:53:09.5420704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5421476Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:09.5421551Z         
2025-03-14T04:53:09.5421826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:09.5422026Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:53:09.5422092Z         
2025-03-14T04:53:09.5422485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:09.5423347Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:09.5423418Z         
2025-03-14T04:53:09.5423787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:09.5424774Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:09.5424860Z         
2025-03-14T04:53:09.5425218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:09.5425427Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:09.5425580Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:09.5425648Z         
2025-03-14T04:53:09.5426074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:09.5426236Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:53:09.5426420Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:09.5426596Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:09.5426676Z         
2025-03-14T04:53:09.5427073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:09.5427286Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:09.5427351Z         
2025-03-14T04:53:09.5427786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:09.5427942Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:09.5428099Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:09.5428240Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:09.5428315Z         
2025-03-14T04:53:09.5428686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:09.5428865Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:09.5428930Z         
2025-03-14T04:53:09.5429250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:09.5429396Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:09.5429470Z         
2025-03-14T04:53:09.5429785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:09.5429928Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:09.5430054Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.5430204Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:09.5430269Z         
2025-03-14T04:53:09.5430593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:09.5430714Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:09.5430876Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:09.5431025Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:09.5431098Z         
2025-03-14T04:53:09.5431407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:09.5431563Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.5431652Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:09.5431811Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:09.5431875Z         
2025-03-14T04:53:09.5432195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:09.5432342Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:09.5432442Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:09.5432570Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:09.5432645Z         
2025-03-14T04:53:09.5433006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:09.5433161Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:09.5433284Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:09.5433349Z         
2025-03-14T04:53:09.5433655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:09.5433811Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:09.5433930Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:09.5433994Z         
2025-03-14T04:53:09.5434297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:09.5434453Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:09.5434573Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:09.5434641Z         
2025-03-14T04:53:09.5434946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:09.5435126Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:09.5435249Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:09.5435315Z         
2025-03-14T04:53:09.5435659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:09.5435801Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:09.5435874Z         
2025-03-14T04:53:09.5436202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:09.5436348Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:09.5436414Z         
2025-03-14T04:53:09.5436760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:09.5436930Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:09.5437064Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:09.5437215Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:09.5437392Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:09.5437458Z         
2025-03-14T04:53:09.5437805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:09.5437942Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:09.5438074Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:09.5438227Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:09.5438370Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:09.5438436Z         
2025-03-14T04:53:09.5438771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:09.5438899Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:09.5439059Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:09.5439195Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:09.5439260Z         
2025-03-14T04:53:09.5439602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:09.5439721Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:09.5439895Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:09.5440028Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:09.5440102Z         
2025-03-14T04:53:09.5440414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:09.5440522Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:09.5440640Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:09.5440713Z         
2025-03-14T04:53:09.5441021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:09.5441124Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:09.5441238Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:09.5441312Z         
2025-03-14T04:53:09.5441615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:09.5441740Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:09.5441867Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:09.5441940Z         
2025-03-14T04:53:09.5442237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:09.5442355Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:09.5442518Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:09.5442594Z         
2025-03-14T04:53:09.5442937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:09.5443155Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:09.5443221Z         
2025-03-14T04:53:09.5443566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:09.5443725Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:09.5443799Z         
2025-03-14T04:53:09.5444182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:09.5444364Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:09.5444428Z         
2025-03-14T04:53:09.5444921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:09.5445058Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:09.5445131Z         
2025-03-14T04:53:09.5445425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.5445572Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:09.5445636Z         
2025-03-14T04:53:09.5446075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:09.5446190Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:09.5446307Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:09.5446422Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:09.5446496Z         
2025-03-14T04:53:09.5446949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:09.5447122Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:09.5447364Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:09.5447430Z         
2025-03-14T04:53:09.5447886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:09.5448057Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:09.5448128Z         
2025-03-14T04:53:09.5448424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.5448584Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:09.5448650Z         
2025-03-14T04:53:09.5449076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:09.5449223Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:09.5449296Z         
2025-03-14T04:53:09.5449624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.5449778Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:09.5449842Z         
2025-03-14T04:53:09.5450230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:09.5450369Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:09.5450442Z         
2025-03-14T04:53:09.5450924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:09.5451068Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:09.5451192Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:09.5451355Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:09.5451486Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:09.5451560Z         
2025-03-14T04:53:09.5451925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:09.5452056Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:09.5452122Z         
2025-03-14T04:53:09.5452129Z 
2025-03-14T04:53:09.5452232Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:09.5544294Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", 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"f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:09.5545103Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:53:09.5545445Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5545884Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5546291Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5546673Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5547052Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5547420Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5547833Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5548245Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5548639Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5549014Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5549385Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5549822Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5550237Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5550624Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5551031Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5551389Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5551806Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5552222Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5552619Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5552993Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5553368Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.5553813Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5554230Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5554636Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.5555045Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.5555359Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5555757Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5556165Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5556557Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5556923Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5557248Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5557680Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5558089Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5558516Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5558878Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5559197Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5559588Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5559990Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5560381Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5560745Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5561067Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5561457Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5561832Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5562175Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5562520Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5562839Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5563223Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5563592Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5563952Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5564308Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5564662Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5565053Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5565460Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5565824Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5566164Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5566497Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5566877Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5567263Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5567626Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5567967Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5568296Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5568673Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5569051Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5569402Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5569761Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5570082Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5570467Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5570839Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5571188Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5571535Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5571895Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.5572292Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5572703Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5573070Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.5573385Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.5573696Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5574079Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5574442Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5574794Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5575133Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5575447Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5575817Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5576182Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5576525Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5576877Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5577186Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5577555Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5577930Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5578272Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5578657Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5578967Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5579376Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5579741Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5580098Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5580449Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5580758Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5581136Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5581738Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5582102Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5582444Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5582765Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5583132Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5583478Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5583811Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5584162Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5584470Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5584815Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5585157Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5585565Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5585886Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5586167Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5586557Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5586899Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5587221Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5587538Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5587824Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5588168Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5588508Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5588850Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5589177Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5589489Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5589866Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5590218Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5590565Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5590891Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5591199Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5591556Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5591919Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5592291Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5592634Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5592970Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5593329Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5593692Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5594029Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5594363Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5594680Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:53:09.5595058Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5595426Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5595787Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:53:09.5596136Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:53:09.5596437Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5596813Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5597170Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5597511Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5597834Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5598144Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5598488Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5598876Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5599212Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5599574Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5599876Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5600224Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5600583Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5600921Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5601246Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5601533Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5601880Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5602227Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5602549Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5602870Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5603157Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5603505Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5603844Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5604173Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5604487Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5604783Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5605131Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5605503Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5605829Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5606174Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5606466Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5606809Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5607155Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5607473Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5607793Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5608102Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5608463Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5608822Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5609153Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5609493Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5609797Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5610164Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5610514Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5610853Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5611190Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5611497Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5611902Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5612243Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5612618Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5612945Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5613254Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5613613Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5613965Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5614302Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5614619Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5614916Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5615265Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5615619Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5615946Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5616272Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5616570Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5616939Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5617288Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5617615Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5617938Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5618228Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5618633Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5618982Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5619346Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5619667Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5619971Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5620322Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5620667Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5621004Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5621321Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5621621Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5621970Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5622324Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5622650Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5622974Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5623272Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5623619Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5623970Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5624348Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5624682Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5625023Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5625395Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5625784Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5626126Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5626456Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5626757Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5627120Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5627471Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5627810Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5628131Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5628439Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5628796Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5629154Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5629495Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5629817Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5630121Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5630473Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5630832Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5631166Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5631531Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5631821Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5632173Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5632557Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5632886Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5633221Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5633514Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5633875Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5634223Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5634563Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5634893Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5635186Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5635546Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5635886Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5636217Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5636535Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5636837Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5637188Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5637542Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5637881Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5638236Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5638537Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5638920Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5639274Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5639600Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5639932Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5640227Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5640591Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5640947Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5641275Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5641604Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5641900Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5642266Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5642608Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5642949Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5643271Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5643576Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5643937Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5644297Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5644661Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5644982Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5645315Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5645674Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5646037Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5646366Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5646695Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5647002Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5647359Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5647723Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5648064Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5648391Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5648686Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5649056Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5649406Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5649743Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5650071Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5650364Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5650730Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5651122Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5651464Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5651799Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5652136Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5652499Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5652866Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5653208Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5653529Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5653831Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5654185Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5654546Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5654872Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5655200Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5655494Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5655868Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5656236Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5656564Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5656891Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5657182Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5657546Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5657935Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5658272Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5658625Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5658927Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5659300Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5659665Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5660008Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5660333Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5660639Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5661006Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5661361Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5661696Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5662032Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5662338Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5662705Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5663063Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5663398Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5663730Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5664030Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5664502Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5664853Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5665225Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5665558Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5665856Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5666224Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5666572Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5666911Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5667232Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5667540Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5667894Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5668254Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5668596Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5668913Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5669217Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5669567Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5669920Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5670252Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5670585Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5670918Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5671281Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5671670Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5672004Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5672333Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5672631Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5672992Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5673345Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5673683Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5674004Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5674308Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5674664Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5675009Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5675345Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5675663Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5675974Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5676328Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5676689Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5677021Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5677393Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5677697Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5678060Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5678454Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5678787Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5679124Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5679422Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5679783Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5680146Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5680481Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5680805Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5681094Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5681572Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5681918Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5682248Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5682565Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5682858Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5683200Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5683545Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5683873Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5684249Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5684546Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5684936Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5685281Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5685599Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5685919Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5686207Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5686558Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5686903Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5687226Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5687549Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5687836Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5688188Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5688523Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5688855Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5689162Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5689459Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5689805Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5690140Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5690525Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5690837Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5691162Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5691505Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5691850Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5692171Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5692491Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5692792Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5693134Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5693479Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5693803Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5694118Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5694411Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5694759Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5695098Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5695425Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5695744Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5696034Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5696384Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5696757Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5697087Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5697436Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5697731Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5698072Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5698422Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5698751Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5699065Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5699357Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5699701Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5700052Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5700433Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5700823Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5701131Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5701524Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5701930Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5702309Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5702653Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5702939Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:53:09.5703323Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5703752Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5704187Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:53:09.5704609Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:53:09.5704929Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:53:09.5705327Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5705726Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5706153Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:53:09.5706518Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:53:09.5706836Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:53:09.5707206Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:53:09.5707572Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:53:09.5707918Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:53:09.5708256Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:53:09.5708631Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:09.5708983Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:09.5709318Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:09.5709711Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:09.5710098Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:09.5710469Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:09.5710870Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:09.5710953Z         
2025-03-14T04:53:09.5711266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5711813Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5711889Z         
2025-03-14T04:53:09.5712197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5713700Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5713779Z         
2025-03-14T04:53:09.5714067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:53:09.5714218Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:53:09.5714283Z         
2025-03-14T04:53:09.5714650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:53:09.5714898Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:53:09.5714966Z         
2025-03-14T04:53:09.5715226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5715640Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5715717Z         
2025-03-14T04:53:09.5715984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5717560Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5717640Z         
2025-03-14T04:53:09.5717934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5718110Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:53:09.5718176Z         
2025-03-14T04:53:09.5718444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5718868Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5718945Z         
2025-03-14T04:53:09.5719215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5720745Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5720829Z         
2025-03-14T04:53:09.5721131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5721287Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:53:09.5721360Z         
2025-03-14T04:53:09.5721629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5722088Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5722165Z         
2025-03-14T04:53:09.5722441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5724020Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5724101Z         
2025-03-14T04:53:09.5724392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5724856Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5724965Z         
2025-03-14T04:53:09.5725248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5726896Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5726980Z         
2025-03-14T04:53:09.5727283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5727436Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:53:09.5727514Z         
2025-03-14T04:53:09.5727810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5727981Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:53:09.5728053Z         
2025-03-14T04:53:09.5728321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5728760Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5728839Z         
2025-03-14T04:53:09.5729113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5730696Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5730777Z         
2025-03-14T04:53:09.5731072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5731267Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:53:09.5731337Z         
2025-03-14T04:53:09.5731601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5732067Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5732143Z         
2025-03-14T04:53:09.5732412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5733977Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5734054Z         
2025-03-14T04:53:09.5734347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5734496Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:53:09.5734566Z         
2025-03-14T04:53:09.5734831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5735272Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5735352Z         
2025-03-14T04:53:09.5735622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5737189Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5737265Z         
2025-03-14T04:53:09.5737546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5737709Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:53:09.5737775Z         
2025-03-14T04:53:09.5738104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5738254Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:53:09.5738363Z         
2025-03-14T04:53:09.5738618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5739049Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5739114Z         
2025-03-14T04:53:09.5739391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5740934Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5741005Z         
2025-03-14T04:53:09.5741321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5741470Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:53:09.5741546Z         
2025-03-14T04:53:09.5741813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5742278Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5742346Z         
2025-03-14T04:53:09.5742634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5744312Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5744391Z         
2025-03-14T04:53:09.5744701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5744882Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:53:09.5744966Z         
2025-03-14T04:53:09.5745231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5745728Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5745794Z         
2025-03-14T04:53:09.5746066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5747587Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5747657Z         
2025-03-14T04:53:09.5747946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5748105Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:53:09.5748181Z         
2025-03-14T04:53:09.5748462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5748629Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:53:09.5748695Z         
2025-03-14T04:53:09.5748954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5749373Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5749446Z         
2025-03-14T04:53:09.5749719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5751390Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5751493Z         
2025-03-14T04:53:09.5751950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5752128Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:53:09.5752222Z         
2025-03-14T04:53:09.5752483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5752923Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5752990Z         
2025-03-14T04:53:09.5753265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5754778Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5754857Z         
2025-03-14T04:53:09.5755146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5755298Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:53:09.5755364Z         
2025-03-14T04:53:09.5755624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5756057Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5756123Z         
2025-03-14T04:53:09.5756393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5757912Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5757990Z         
2025-03-14T04:53:09.5758275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5758726Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5758828Z         
2025-03-14T04:53:09.5759092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5760690Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5760764Z         
2025-03-14T04:53:09.5761043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5761198Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:53:09.5761263Z         
2025-03-14T04:53:09.5761553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5761708Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:53:09.5761781Z         
2025-03-14T04:53:09.5762032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5762465Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5762532Z         
2025-03-14T04:53:09.5762803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5764333Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5764411Z         
2025-03-14T04:53:09.5764703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5764877Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:53:09.5764952Z         
2025-03-14T04:53:09.5765201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5765637Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5765749Z         
2025-03-14T04:53:09.5766024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5767524Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5767598Z         
2025-03-14T04:53:09.5767889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5768031Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:53:09.5768104Z         
2025-03-14T04:53:09.5768355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5768796Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5768864Z         
2025-03-14T04:53:09.5769136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5770641Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5770719Z         
2025-03-14T04:53:09.5771005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5771160Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:53:09.5771232Z         
2025-03-14T04:53:09.5771545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5771705Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:53:09.5771770Z         
2025-03-14T04:53:09.5772057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5772475Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5772548Z         
2025-03-14T04:53:09.5772822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5774423Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5774500Z         
2025-03-14T04:53:09.5774788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5774937Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:53:09.5775003Z         
2025-03-14T04:53:09.5775261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5775688Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5775761Z         
2025-03-14T04:53:09.5776023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5777549Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5777626Z         
2025-03-14T04:53:09.5777910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5778095Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:53:09.5778165Z         
2025-03-14T04:53:09.5778421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5778875Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5778953Z         
2025-03-14T04:53:09.5779220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5780750Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5780828Z         
2025-03-14T04:53:09.5781113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5781280Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:53:09.5781347Z         
2025-03-14T04:53:09.5781753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5781910Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:53:09.5781992Z         
2025-03-14T04:53:09.5782242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5782670Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5782738Z         
2025-03-14T04:53:09.5783014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5784645Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5784725Z         
2025-03-14T04:53:09.5785122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5785272Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:53:09.5785406Z         
2025-03-14T04:53:09.5785673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5786133Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5786205Z         
2025-03-14T04:53:09.5786496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5788120Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5788199Z         
2025-03-14T04:53:09.5788516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5788667Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:53:09.5788746Z         
2025-03-14T04:53:09.5789011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5789478Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5789552Z         
2025-03-14T04:53:09.5789841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5791472Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5791547Z         
2025-03-14T04:53:09.5791887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5792052Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:53:09.5792128Z         
2025-03-14T04:53:09.5792425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5792659Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:53:09.5792729Z         
2025-03-14T04:53:09.5793006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5793453Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5793524Z         
2025-03-14T04:53:09.5793812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5795405Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5795488Z         
2025-03-14T04:53:09.5795792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5795947Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:53:09.5796014Z         
2025-03-14T04:53:09.5796290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5796739Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5796807Z         
2025-03-14T04:53:09.5797098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5798688Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5798803Z         
2025-03-14T04:53:09.5799105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5799256Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:53:09.5799364Z         
2025-03-14T04:53:09.5799637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5800100Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5800168Z         
2025-03-14T04:53:09.5800456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5802050Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5802129Z         
2025-03-14T04:53:09.5802404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5802860Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:53:09.5802940Z         
2025-03-14T04:53:09.5803215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5804872Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5804953Z         
2025-03-14T04:53:09.5805249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5805405Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:53:09.5805473Z         
2025-03-14T04:53:09.5805810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5805963Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:53:09.5806040Z         
2025-03-14T04:53:09.5806310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5806781Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5806851Z         
2025-03-14T04:53:09.5807134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5808730Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5808810Z         
2025-03-14T04:53:09.5809115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5809260Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:53:09.5809336Z         
2025-03-14T04:53:09.5809600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5810054Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5810123Z         
2025-03-14T04:53:09.5810406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5812004Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5812084Z         
2025-03-14T04:53:09.5812391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5812534Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:53:09.5812642Z         
2025-03-14T04:53:09.5812909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5813358Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5813468Z         
2025-03-14T04:53:09.5813754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5815346Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5815425Z         
2025-03-14T04:53:09.5815726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5815879Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:53:09.5815955Z         
2025-03-14T04:53:09.5816251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5816438Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:53:09.5816509Z         
2025-03-14T04:53:09.5816781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5817210Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5817285Z         
2025-03-14T04:53:09.5817562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5819160Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5819239Z         
2025-03-14T04:53:09.5819569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5819718Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:53:09.5819787Z         
2025-03-14T04:53:09.5820061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5820531Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5820609Z         
2025-03-14T04:53:09.5820891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5822494Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5822575Z         
2025-03-14T04:53:09.5822881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5823035Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:53:09.5823107Z         
2025-03-14T04:53:09.5823388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5823835Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5823915Z         
2025-03-14T04:53:09.5824252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5825905Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5825984Z         
2025-03-14T04:53:09.5826278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5826474Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:53:09.5826544Z         
2025-03-14T04:53:09.5826848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5826998Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:53:09.5827115Z         
2025-03-14T04:53:09.5827377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5827817Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5827887Z         
2025-03-14T04:53:09.5828172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5829745Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5829811Z         
2025-03-14T04:53:09.5830105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5830240Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:53:09.5830316Z         
2025-03-14T04:53:09.5830565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5830989Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5831054Z         
2025-03-14T04:53:09.5831323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5832816Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5832885Z         
2025-03-14T04:53:09.5833209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5833345Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:53:09.5833421Z         
2025-03-14T04:53:09.5833671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5834127Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5834193Z         
2025-03-14T04:53:09.5834475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5835993Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5836061Z         
2025-03-14T04:53:09.5836352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5836498Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:53:09.5836571Z         
2025-03-14T04:53:09.5836851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5837006Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:53:09.5837071Z         
2025-03-14T04:53:09.5837326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5837740Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5837814Z         
2025-03-14T04:53:09.5838088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5839623Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5839704Z         
2025-03-14T04:53:09.5839990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5840133Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:53:09.5840229Z         
2025-03-14T04:53:09.5840490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5840912Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5840986Z         
2025-03-14T04:53:09.5841268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5842826Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5842904Z         
2025-03-14T04:53:09.5843200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5843345Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:53:09.5843413Z         
2025-03-14T04:53:09.5843678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5844127Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5844196Z         
2025-03-14T04:53:09.5844482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5846099Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5846179Z         
2025-03-14T04:53:09.5846501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5846665Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:53:09.5846736Z         
2025-03-14T04:53:09.5847043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5847230Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:53:09.5847299Z         
2025-03-14T04:53:09.5847570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5848006Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5848081Z         
2025-03-14T04:53:09.5848358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5849965Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5850048Z         
2025-03-14T04:53:09.5850347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5850498Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:53:09.5850566Z         
2025-03-14T04:53:09.5850839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5851278Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5851355Z         
2025-03-14T04:53:09.5851636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5853274Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5853357Z         
2025-03-14T04:53:09.5853660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5853806Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:53:09.5853913Z         
2025-03-14T04:53:09.5854178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5854618Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5854695Z         
2025-03-14T04:53:09.5854971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5856616Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5856710Z         
2025-03-14T04:53:09.5857009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5857168Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:53:09.5857241Z         
2025-03-14T04:53:09.5857546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5857695Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:53:09.5857776Z         
2025-03-14T04:53:09.5858038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5858498Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5858565Z         
2025-03-14T04:53:09.5858844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5860429Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5860505Z         
2025-03-14T04:53:09.5860801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5860969Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:53:09.5861045Z         
2025-03-14T04:53:09.5861300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5861739Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5861806Z         
2025-03-14T04:53:09.5862092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5863610Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5863687Z         
2025-03-14T04:53:09.5863987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5864201Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:53:09.5864284Z         
2025-03-14T04:53:09.5864553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5865009Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5865077Z         
2025-03-14T04:53:09.5865363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5866919Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5867002Z         
2025-03-14T04:53:09.5867292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5867440Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:53:09.5867549Z         
2025-03-14T04:53:09.5867835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5867984Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:53:09.5868051Z         
2025-03-14T04:53:09.5868304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5868713Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5868787Z         
2025-03-14T04:53:09.5869050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5870553Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5870630Z         
2025-03-14T04:53:09.5870912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5871053Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:53:09.5871118Z         
2025-03-14T04:53:09.5871373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5871788Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5871861Z         
2025-03-14T04:53:09.5872122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5873658Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5873733Z         
2025-03-14T04:53:09.5874018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5874187Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:53:09.5874252Z         
2025-03-14T04:53:09.5874513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5874937Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5875011Z         
2025-03-14T04:53:09.5875278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5876823Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5876902Z         
2025-03-14T04:53:09.5877199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5877356Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:53:09.5877424Z         
2025-03-14T04:53:09.5877729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5877870Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:53:09.5877947Z         
2025-03-14T04:53:09.5878212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5878657Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5878727Z         
2025-03-14T04:53:09.5879017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5880625Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5880725Z         
2025-03-14T04:53:09.5881040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5881189Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:53:09.5881269Z         
2025-03-14T04:53:09.5881665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5882100Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5882167Z         
2025-03-14T04:53:09.5882438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5883955Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5884023Z         
2025-03-14T04:53:09.5884318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5884457Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:53:09.5884533Z         
2025-03-14T04:53:09.5884780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5885212Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5885278Z         
2025-03-14T04:53:09.5885549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5887124Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5887194Z         
2025-03-14T04:53:09.5887481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5887678Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:53:09.5887751Z         
2025-03-14T04:53:09.5888034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5888183Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:53:09.5888249Z         
2025-03-14T04:53:09.5888506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5888930Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5889000Z         
2025-03-14T04:53:09.5889273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5890778Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5890853Z         
2025-03-14T04:53:09.5891133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5891279Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:53:09.5891345Z         
2025-03-14T04:53:09.5891599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5892028Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5892096Z         
2025-03-14T04:53:09.5892364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5893909Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5894012Z         
2025-03-14T04:53:09.5894297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5894442Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:53:09.5894513Z         
2025-03-14T04:53:09.5894762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5895193Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5895259Z         
2025-03-14T04:53:09.5895533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5897042Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5897117Z         
2025-03-14T04:53:09.5897427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5897586Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:53:09.5897665Z         
2025-03-14T04:53:09.5897970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5898127Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:53:09.5898198Z         
2025-03-14T04:53:09.5898466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5898912Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5898991Z         
2025-03-14T04:53:09.5899276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5900920Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5901065Z         
2025-03-14T04:53:09.5901378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5901529Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:53:09.5901596Z         
2025-03-14T04:53:09.5901880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5902341Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5902422Z         
2025-03-14T04:53:09.5902712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5904380Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5904468Z         
2025-03-14T04:53:09.5904771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5904924Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:53:09.5904994Z         
2025-03-14T04:53:09.5905267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5905724Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5905803Z         
2025-03-14T04:53:09.5906081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5907787Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5907899Z         
2025-03-14T04:53:09.5908200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5908368Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:53:09.5908439Z         
2025-03-14T04:53:09.5908749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5908905Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:53:09.5908983Z         
2025-03-14T04:53:09.5909248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5909697Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5909769Z         
2025-03-14T04:53:09.5910052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5911685Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5911758Z         
2025-03-14T04:53:09.5912068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5912216Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:53:09.5912296Z         
2025-03-14T04:53:09.5912564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5913016Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5913090Z         
2025-03-14T04:53:09.5913375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5914988Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5915089Z         
2025-03-14T04:53:09.5915389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5915526Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:53:09.5915599Z         
2025-03-14T04:53:09.5915857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5916296Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5916366Z         
2025-03-14T04:53:09.5916643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5918204Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5918273Z         
2025-03-14T04:53:09.5918564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5918714Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:53:09.5918790Z         
2025-03-14T04:53:09.5919081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5919233Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:53:09.5919299Z         
2025-03-14T04:53:09.5919566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5919990Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5920067Z         
2025-03-14T04:53:09.5920335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5921927Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5922048Z         
2025-03-14T04:53:09.5922347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5922494Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:53:09.5922561Z         
2025-03-14T04:53:09.5922836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5923270Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5923345Z         
2025-03-14T04:53:09.5923621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5925249Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5925328Z         
2025-03-14T04:53:09.5925625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5925774Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:53:09.5925840Z         
2025-03-14T04:53:09.5926108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5926543Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5926618Z         
2025-03-14T04:53:09.5926899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5928509Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5928626Z         
2025-03-14T04:53:09.5928910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5929073Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:53:09.5929143Z         
2025-03-14T04:53:09.5929461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5929605Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:53:09.5929683Z         
2025-03-14T04:53:09.5929936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5930381Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5930457Z         
2025-03-14T04:53:09.5930726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5932311Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5932382Z         
2025-03-14T04:53:09.5932682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5932829Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:53:09.5932898Z         
2025-03-14T04:53:09.5933160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5933592Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5933667Z         
2025-03-14T04:53:09.5933973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5935502Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5935610Z         
2025-03-14T04:53:09.5935895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5936038Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:53:09.5936102Z         
2025-03-14T04:53:09.5936361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5936776Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5936847Z         
2025-03-14T04:53:09.5937105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5938620Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5938694Z         
2025-03-14T04:53:09.5938973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5939128Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:53:09.5939193Z         
2025-03-14T04:53:09.5939480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5939622Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:53:09.5939694Z         
2025-03-14T04:53:09.5939942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5940388Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5940455Z         
2025-03-14T04:53:09.5940722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5942230Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5942332Z         
2025-03-14T04:53:09.5942619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5942755Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:53:09.5942826Z         
2025-03-14T04:53:09.5943070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5943490Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5943554Z         
2025-03-14T04:53:09.5943831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5945487Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5945572Z         
2025-03-14T04:53:09.5945881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5946022Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:53:09.5946099Z         
2025-03-14T04:53:09.5946362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5946813Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5946883Z         
2025-03-14T04:53:09.5947205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5948839Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5948941Z         
2025-03-14T04:53:09.5949248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5949407Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:53:09.5949486Z         
2025-03-14T04:53:09.5949782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5949940Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:53:09.5950014Z         
2025-03-14T04:53:09.5950290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5950740Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5950824Z         
2025-03-14T04:53:09.5951103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5952744Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5952828Z         
2025-03-14T04:53:09.5953133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5953287Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:53:09.5953358Z         
2025-03-14T04:53:09.5953629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5954120Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5954199Z         
2025-03-14T04:53:09.5954473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5956123Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5956202Z         
2025-03-14T04:53:09.5956505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5956658Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:53:09.5956726Z         
2025-03-14T04:53:09.5957000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5957447Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5957528Z         
2025-03-14T04:53:09.5957808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5959425Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5959506Z         
2025-03-14T04:53:09.5959807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5959976Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:53:09.5960044Z         
2025-03-14T04:53:09.5960353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5960503Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:53:09.5960579Z         
2025-03-14T04:53:09.5960847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5961322Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5961431Z         
2025-03-14T04:53:09.5961722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5963346Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5963417Z         
2025-03-14T04:53:09.5963732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5963878Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:53:09.5963957Z         
2025-03-14T04:53:09.5964226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5964678Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5964754Z         
2025-03-14T04:53:09.5965036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5966654Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5966726Z         
2025-03-14T04:53:09.5967044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5967187Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:53:09.5967262Z         
2025-03-14T04:53:09.5967531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5968016Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5968095Z         
2025-03-14T04:53:09.5968372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5970009Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5970077Z         
2025-03-14T04:53:09.5970382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5970546Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:53:09.5970615Z         
2025-03-14T04:53:09.5970917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5971066Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:53:09.5971142Z         
2025-03-14T04:53:09.5971426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5971904Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5971976Z         
2025-03-14T04:53:09.5972263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5973866Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5973944Z         
2025-03-14T04:53:09.5974252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5974395Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:53:09.5974470Z         
2025-03-14T04:53:09.5974771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5975221Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5975321Z         
2025-03-14T04:53:09.5975606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5977205Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5977286Z         
2025-03-14T04:53:09.5977595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5977736Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:53:09.5977814Z         
2025-03-14T04:53:09.5978075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5978526Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5978597Z         
2025-03-14T04:53:09.5978882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5980486Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5980567Z         
2025-03-14T04:53:09.5980868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5981023Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:53:09.5981101Z         
2025-03-14T04:53:09.5981396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5981751Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:53:09.5981829Z         
2025-03-14T04:53:09.5982103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5982540Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5982661Z         
2025-03-14T04:53:09.5982944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5984667Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5984759Z         
2025-03-14T04:53:09.5985086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5985245Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:53:09.5985312Z         
2025-03-14T04:53:09.5985591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5986046Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5986126Z         
2025-03-14T04:53:09.5986409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5988060Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5988139Z         
2025-03-14T04:53:09.5988442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5988601Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:53:09.5988670Z         
2025-03-14T04:53:09.5988979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5989434Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.5989540Z         
2025-03-14T04:53:09.5989818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5991474Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5991556Z         
2025-03-14T04:53:09.5991850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.5992020Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:53:09.5992089Z         
2025-03-14T04:53:09.5992395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5992550Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:53:09.5992629Z         
2025-03-14T04:53:09.5992893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5993346Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.5993416Z         
2025-03-14T04:53:09.5993702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5995345Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5995419Z         
2025-03-14T04:53:09.5995723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5995901Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:53:09.5995979Z         
2025-03-14T04:53:09.5996240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.5996724Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.5996792Z         
2025-03-14T04:53:09.5997079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.5998694Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.5998766Z         
2025-03-14T04:53:09.5999075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.5999222Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:53:09.5999298Z         
2025-03-14T04:53:09.5999558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6000020Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.6000090Z         
2025-03-14T04:53:09.6000373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6001990Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6002063Z         
2025-03-14T04:53:09.6002365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.6002535Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:53:09.6002643Z         
2025-03-14T04:53:09.6002944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6003106Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:53:09.6003228Z         
2025-03-14T04:53:09.6003501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6003945Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.6004013Z         
2025-03-14T04:53:09.6004301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6005894Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6005974Z         
2025-03-14T04:53:09.6006275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6006432Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:53:09.6006501Z         
2025-03-14T04:53:09.6006773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6007231Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.6007300Z         
2025-03-14T04:53:09.6007587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6009203Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6009283Z         
2025-03-14T04:53:09.6009636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6009790Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:53:09.6009869Z         
2025-03-14T04:53:09.6010134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6010627Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.6010696Z         
2025-03-14T04:53:09.6010980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6012585Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6012665Z         
2025-03-14T04:53:09.6012964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.6013129Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:53:09.6013205Z         
2025-03-14T04:53:09.6013499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6013661Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:53:09.6013729Z         
2025-03-14T04:53:09.6014000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6014446Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.6014522Z         
2025-03-14T04:53:09.6014799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6016411Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6016487Z         
2025-03-14T04:53:09.6016769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6016946Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:53:09.6017011Z         
2025-03-14T04:53:09.6017266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6017701Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.6017776Z         
2025-03-14T04:53:09.6018047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6019609Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6019688Z         
2025-03-14T04:53:09.6019985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6020136Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:53:09.6020209Z         
2025-03-14T04:53:09.6020482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6020930Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.6021009Z         
2025-03-14T04:53:09.6021290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6022903Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6022982Z         
2025-03-14T04:53:09.6023308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.6023482Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:53:09.6023583Z         
2025-03-14T04:53:09.6023890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6024047Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:53:09.6024220Z         
2025-03-14T04:53:09.6024504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6024977Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:53:09.6025050Z         
2025-03-14T04:53:09.6025355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6027024Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6027097Z         
2025-03-14T04:53:09.6027421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6027577Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:53:09.6027658Z         
2025-03-14T04:53:09.6027933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6028422Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:53:09.6028495Z         
2025-03-14T04:53:09.6028798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6030507Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6030584Z         
2025-03-14T04:53:09.6030938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6031139Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:53:09.6031219Z         
2025-03-14T04:53:09.6031499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6032005Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:53:09.6032081Z         
2025-03-14T04:53:09.6032385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:53:09.6034007Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:53:09.6034076Z         
2025-03-14T04:53:09.6034366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:53:09.6034530Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:53:09.6034606Z         
2025-03-14T04:53:09.6034902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:53:09.6035063Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:53:09.6035132Z         
2025-03-14T04:53:09.6035615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.6035781Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.6035860Z         
2025-03-14T04:53:09.6036172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.6036330Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:09.6036400Z         
2025-03-14T04:53:09.6036866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:09.6037032Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:09.6037102Z         
2025-03-14T04:53:09.6037444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.6037601Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:09.6037680Z         
2025-03-14T04:53:09.6038107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:09.6038307Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:09.6038414Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:09.6038553Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:09.6038632Z         
2025-03-14T04:53:09.6038976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:09.6039109Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:09.6039188Z         
2025-03-14T04:53:09.6039533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:09.6039671Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:09.6039739Z         
2025-03-14T04:53:09.6040146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:09.6040371Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:09.6040472Z         
2025-03-14T04:53:09.6040918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:09.6041053Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:09.6041491Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:09.6041630Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:09.6041749Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:09.6041825Z         
2025-03-14T04:53:09.6042133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.6042275Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T04:53:09.6042341Z         
2025-03-14T04:53:09.6042614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:09.6043406Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:09.6043482Z         
2025-03-14T04:53:09.6043764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:09.6043986Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T04:53:09.6044061Z         
2025-03-14T04:53:09.6044457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:09.6045361Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:09.6045428Z         
2025-03-14T04:53:09.6045799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:09.6046601Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:09.6046677Z         
2025-03-14T04:53:09.6047023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:09.6047178Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:09.6047326Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:09.6047392Z         
2025-03-14T04:53:09.6047818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:09.6047979Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T04:53:09.6048156Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:09.6048334Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:09.6048405Z         
2025-03-14T04:53:09.6048808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:09.6049016Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:09.6049084Z         
2025-03-14T04:53:09.6049514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:09.6049665Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:09.6049827Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:09.6049973Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:09.6050052Z         
2025-03-14T04:53:09.6050474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:09.6050656Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:09.6050749Z         
2025-03-14T04:53:09.6051072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:09.6051213Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:09.6051289Z         
2025-03-14T04:53:09.6051601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:09.6051739Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:09.6051868Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.6052019Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:09.6052085Z         
2025-03-14T04:53:09.6052415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:09.6052554Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:09.6052680Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:09.6052837Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:09.6052904Z         
2025-03-14T04:53:09.6053231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:09.6053360Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:09.6053461Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:09.6053587Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:09.6053662Z         
2025-03-14T04:53:09.6053982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:09.6054138Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:09.6054231Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:09.6054372Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:09.6054440Z         
2025-03-14T04:53:09.6054816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:09.6054978Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:09.6055105Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:09.6055172Z         
2025-03-14T04:53:09.6055487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:09.6055655Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:09.6055775Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:09.6055840Z         
2025-03-14T04:53:09.6056145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:09.6056345Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:09.6056469Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:09.6056536Z         
2025-03-14T04:53:09.6056847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:09.6057060Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:09.6057180Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:09.6057246Z         
2025-03-14T04:53:09.6057591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:09.6057732Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:09.6057806Z         
2025-03-14T04:53:09.6058140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:09.6058284Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:09.6058352Z         
2025-03-14T04:53:09.6058703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:09.6058844Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:09.6058977Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:09.6059127Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:09.6059276Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:09.6059342Z         
2025-03-14T04:53:09.6059694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:09.6059842Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:09.6059963Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:09.6060117Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:09.6060253Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:09.6060327Z         
2025-03-14T04:53:09.6060655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:09.6060783Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:09.6060944Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:09.6061084Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:09.6061152Z         
2025-03-14T04:53:09.6061491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:09.6061606Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:09.6061781Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:09.6061914Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:09.6061988Z         
2025-03-14T04:53:09.6062331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:09.6062442Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:09.6062562Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:09.6062668Z         
2025-03-14T04:53:09.6062986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:09.6063092Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:09.6063210Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:09.6063286Z         
2025-03-14T04:53:09.6063594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:09.6063721Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:09.6063852Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:09.6063926Z         
2025-03-14T04:53:09.6064309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:09.6064444Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:09.6064572Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:09.6064651Z         
2025-03-14T04:53:09.6065006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:09.6065201Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:09.6065274Z         
2025-03-14T04:53:09.6065624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:09.6065788Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:09.6065868Z         
2025-03-14T04:53:09.6066246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:09.6066424Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:09.6066490Z         
2025-03-14T04:53:09.6066978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:09.6067113Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:09.6067187Z         
2025-03-14T04:53:09.6067479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.6067628Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:09.6067693Z         
2025-03-14T04:53:09.6068129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:09.6068251Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:09.6068357Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:09.6068521Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:09.6068589Z         
2025-03-14T04:53:09.6069049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:09.6069250Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:09.6069492Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:09.6069562Z         
2025-03-14T04:53:09.6070036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:09.6070209Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:09.6070286Z         
2025-03-14T04:53:09.6070585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:09.6070781Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:09.6070851Z         
2025-03-14T04:53:09.6071246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:09.6071396Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:09.6071472Z         
2025-03-14T04:53:09.6071770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:09.6071931Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:09.6072001Z         
2025-03-14T04:53:09.6072387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:09.6072534Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:09.6072610Z         
2025-03-14T04:53:09.6073096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:09.6073245Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:09.6073371Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:09.6073538Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:09.6073673Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:09.6073750Z         
2025-03-14T04:53:09.6074123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:09.6074253Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:09.6074320Z         
2025-03-14T04:53:28.1729525Z 
2025-03-14T04:53:28.1730584Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:28.1733201Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:28.1735053Z         l_features_res4_ = L_features_res4_
2025-03-14T04:53:28.1735473Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:28.1736016Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:28.1736514Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:28.1737061Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:28.1737799Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:28.1738484Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:28.1739048Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:28.1739427Z         
2025-03-14T04:53:28.1740029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:28.1740869Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:28.1741153Z         
2025-03-14T04:53:28.1741556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1742064Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:28.1742328Z         
2025-03-14T04:53:28.1742866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:28.1743511Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:28.1743787Z         
2025-03-14T04:53:28.1744293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1744829Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:28.1745104Z         
2025-03-14T04:53:28.1745625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:28.1746263Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:28.1746619Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:28.1746905Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:28.1747157Z         
2025-03-14T04:53:28.1747607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:28.1748151Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:28.1748468Z         
2025-03-14T04:53:28.1748924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:28.1749457Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:28.1749743Z         
2025-03-14T04:53:28.1750212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:28.1750885Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:28.1751235Z         
2025-03-14T04:53:28.1751745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:28.1752344Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:28.1752855Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:28.1753352Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:28.1753647Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:28.1753887Z         
2025-03-14T04:53:28.1754295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:28.1754777Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:53:28.1755029Z         
2025-03-14T04:53:28.1755393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:28.1756314Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:28.1757016Z         
2025-03-14T04:53:28.1757381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:28.1757898Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:28.1758200Z         
2025-03-14T04:53:28.1758673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:28.1759740Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:28.1760486Z         
2025-03-14T04:53:28.1760936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:28.1761979Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:28.1762691Z         
2025-03-14T04:53:28.1763117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:28.1763692Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:28.1764041Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:28.1764302Z         
2025-03-14T04:53:28.1764807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:28.1765430Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:53:28.1765813Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:28.1766212Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:28.1766514Z         
2025-03-14T04:53:28.1766999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:28.1767655Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:28.1767976Z         
2025-03-14T04:53:28.1768491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:28.1769122Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:28.1769474Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:28.1769815Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:28.1770076Z         
2025-03-14T04:53:28.1770538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:28.1771133Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:28.1771425Z         
2025-03-14T04:53:28.1771825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:28.1772331Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:28.1772590Z         
2025-03-14T04:53:28.1772989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:28.1773488Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:28.1773798Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:28.1774123Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:28.1774386Z         
2025-03-14T04:53:28.1774792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:28.1775285Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:28.1775617Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:28.1775945Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:28.1776204Z         
2025-03-14T04:53:28.1776630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:28.1777119Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:28.1777385Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:28.1777652Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:28.1777898Z         
2025-03-14T04:53:28.1778297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:28.1778810Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:28.1779103Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:28.1779378Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:28.1779627Z         
2025-03-14T04:53:28.1780060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:28.1780559Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:28.1780883Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:28.1781124Z         
2025-03-14T04:53:28.1781839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:28.1782370Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:28.1782703Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:28.1782951Z         
2025-03-14T04:53:28.1783365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:28.1783931Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:28.1784482Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:28.1784830Z         
2025-03-14T04:53:28.1785234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:28.1785780Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:28.1786143Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:28.1786383Z         
2025-03-14T04:53:28.1786817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:28.1787369Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:28.1787638Z         
2025-03-14T04:53:28.1788065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:28.1788655Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:28.1789055Z         
2025-03-14T04:53:28.1789664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:28.1790229Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:28.1790557Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:28.1790951Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:28.1791315Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:28.1791582Z         
2025-03-14T04:53:28.1792027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:28.1792580Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:28.1792905Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:28.1793247Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:28.1793600Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:28.1793861Z         
2025-03-14T04:53:28.1794292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:28.1794808Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:28.1795145Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:28.1795501Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:28.1795757Z         
2025-03-14T04:53:28.1796191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:28.1796707Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:28.1797050Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:28.1797416Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:28.1797677Z         
2025-03-14T04:53:28.1798091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:28.1798564Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:28.1798842Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:28.1799077Z         
2025-03-14T04:53:28.1799474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:28.1799926Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:28.1800188Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:28.1800419Z         
2025-03-14T04:53:28.1800813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:28.1801291Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:28.1801587Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:28.1801833Z         
2025-03-14T04:53:28.1802222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:28.1802773Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:28.1803092Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:28.1803344Z         
2025-03-14T04:53:28.1803773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:28.1804394Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:28.1804691Z         
2025-03-14T04:53:28.1805114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:28.1805659Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:28.1805946Z         
2025-03-14T04:53:28.1806418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:28.1807013Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:28.1807300Z         
2025-03-14T04:53:28.1820375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:28.1821384Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:28.1821659Z         
2025-03-14T04:53:28.1822088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1822605Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:28.1822891Z         
2025-03-14T04:53:28.1823433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:28.1824159Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:28.1824440Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:28.1824726Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:28.1824978Z         
2025-03-14T04:53:28.1825556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:28.1826261Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:28.1826743Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:28.1827110Z         
2025-03-14T04:53:28.1827676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:28.1828373Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:28.1828668Z         
2025-03-14T04:53:28.1829071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1829596Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:28.1829880Z         
2025-03-14T04:53:28.1830848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:28.1831461Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:28.1831782Z         
2025-03-14T04:53:28.1832182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:28.1832688Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:28.1832961Z         
2025-03-14T04:53:28.1833435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:28.1834024Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:28.1834297Z         
2025-03-14T04:53:28.1834882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:28.1835577Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:28.1835900Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:28.1836241Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:28.1836595Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:28.1836859Z         
2025-03-14T04:53:28.1837338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:28.1837889Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:28.1838129Z         
2025-03-14T04:53:28.1838223Z 
2025-03-14T04:53:28.1838329Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:28.1839648Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:53:28.1840932Z         l_features_res4_ = L_features_res4_
2025-03-14T04:53:28.1841336Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:53:28.1841867Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:53:28.1842353Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:53:28.1842883Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:53:28.1843467Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:53:28.1844030Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:53:28.1844626Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:53:28.1844999Z         
2025-03-14T04:53:28.1845540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:28.1846202Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:53:28.1846476Z         
2025-03-14T04:53:28.1846866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1847353Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:28.1847611Z         
2025-03-14T04:53:28.1848129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:53:28.1848761Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:53:28.1849032Z         
2025-03-14T04:53:28.1849411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1849894Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:53:28.1850153Z         
2025-03-14T04:53:28.1850615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:53:28.1851215Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:53:28.1851550Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:53:28.1851818Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:53:28.1852055Z         
2025-03-14T04:53:28.1852472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:53:28.1852982Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:53:28.1853226Z         
2025-03-14T04:53:28.1853641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:53:28.1854138Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:53:28.1854382Z         
2025-03-14T04:53:28.1854852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:53:28.1855494Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:53:28.1855827Z         
2025-03-14T04:53:28.1856333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:53:28.1856926Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:53:28.1857429Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:53:28.1857929Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:53:28.1858266Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:53:28.1858504Z         
2025-03-14T04:53:28.1858893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:28.1859393Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:53:28.1859638Z         
2025-03-14T04:53:28.1859988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:53:28.1860898Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:53:28.1861603Z         
2025-03-14T04:53:28.1861966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:53:28.1862480Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:53:28.1862783Z         
2025-03-14T04:53:28.1863252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:53:28.1864418Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:53:28.1865193Z         
2025-03-14T04:53:28.1865651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:53:28.1866683Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:53:28.1867411Z         
2025-03-14T04:53:28.1867849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:53:28.1868407Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:53:28.1868760Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:53:28.1869026Z         
2025-03-14T04:53:28.1869544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:53:28.1870183Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:53:28.1870574Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:53:28.1870977Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:53:28.1871281Z         
2025-03-14T04:53:28.1871820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:28.1872496Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:53:28.1872856Z         
2025-03-14T04:53:28.1873382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:28.1874034Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:53:28.1874396Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:28.1874743Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:28.1875010Z         
2025-03-14T04:53:28.1875482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:28.1876082Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:53:28.1876382Z         
2025-03-14T04:53:28.1876788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:28.1877301Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:28.1877566Z         
2025-03-14T04:53:28.1877974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:28.1878484Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:28.1878792Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:28.1879117Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:28.1879381Z         
2025-03-14T04:53:28.1879784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:28.1880278Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:28.1880574Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:28.1880896Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:28.1881155Z         
2025-03-14T04:53:28.1881779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:28.1882281Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:28.1882553Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:28.1882821Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:53:28.1883070Z         
2025-03-14T04:53:28.1883478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:28.1883990Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:28.1884282Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:28.1884551Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:53:28.1884796Z         
2025-03-14T04:53:28.1885224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:28.1885829Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:28.1886161Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:28.1886402Z         
2025-03-14T04:53:28.1886866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:28.1887369Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:28.1887690Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:28.1887920Z         
2025-03-14T04:53:28.1888305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:28.1888813Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:28.1889134Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:53:28.1889369Z         
2025-03-14T04:53:28.1889758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:28.1890294Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:28.1890649Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:53:28.1890886Z         
2025-03-14T04:53:28.1891311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:28.1891842Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:28.1892103Z         
2025-03-14T04:53:28.1892522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:28.1893041Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:28.1893300Z         
2025-03-14T04:53:28.1893733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:28.1894269Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:28.1894590Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:53:28.1894921Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:28.1895269Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:53:28.1895530Z         
2025-03-14T04:53:28.1895967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:28.1896506Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:28.1896820Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:53:28.1897159Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:28.1897500Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:53:28.1897762Z         
2025-03-14T04:53:28.1898179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:28.1898743Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:28.1899078Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:28.1899419Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:53:28.1899704Z         
2025-03-14T04:53:28.1900122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:28.1900627Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:28.1900964Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:28.1901307Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:53:28.1901561Z         
2025-03-14T04:53:28.1901965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:28.1902434Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:28.1902710Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:28.1902956Z         
2025-03-14T04:53:28.1903358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:28.1903830Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:28.1904167Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:28.1904409Z         
2025-03-14T04:53:28.1904821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:28.1905337Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:28.1905660Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:28.1905916Z         
2025-03-14T04:53:28.1906315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:28.1906795Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:28.1907088Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:28.1907338Z         
2025-03-14T04:53:28.1907781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:28.1908374Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:28.1908680Z         
2025-03-14T04:53:28.1909112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:28.1909671Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:28.1909962Z         
2025-03-14T04:53:28.1910445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:28.1911060Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:28.1911358Z         
2025-03-14T04:53:28.1911979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:28.1912671Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:28.1912934Z         
2025-03-14T04:53:28.1913324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1913853Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:53:28.1914121Z         
2025-03-14T04:53:28.1914645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:28.1915251Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:53:28.1915527Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:28.1915805Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:28.1916041Z         
2025-03-14T04:53:28.1916595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:28.1917286Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:28.1917748Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:53:28.1918101Z         
2025-03-14T04:53:28.1918656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:28.1919347Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:28.1919635Z         
2025-03-14T04:53:28.1920028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:28.1920546Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:28.1920817Z         
2025-03-14T04:53:28.1921283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:28.1921856Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:28.1922114Z         
2025-03-14T04:53:28.1922494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:28.1922987Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:53:28.1923248Z         
2025-03-14T04:53:28.1923708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:28.1924275Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:28.1924533Z         
2025-03-14T04:53:28.1925094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:28.1925760Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:53:28.1926071Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:28.1926431Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:28.1926774Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:28.1927029Z         
2025-03-14T04:53:28.1927516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:28.1928047Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:28.1928284Z         
2025-03-14T04:53:30.7418852Z 
2025-03-14T04:53:30.7422060Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:30.7423867Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:53:30.7425107Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:53:30.7427591Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:53:30.7428098Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:53:30.7433212Z         
2025-03-14T04:53:30.7437109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:53:30.7438057Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:53:30.7439748Z         
2025-03-14T04:53:30.7440385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:53:30.7441149Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:53:30.7446645Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:53:30.7448750Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:53:30.7449211Z         
2025-03-14T04:53:30.7449968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:30.7450739Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:53:30.7451193Z         
2025-03-14T04:53:30.7451659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:30.7452182Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:53:30.7452458Z         
2025-03-14T04:53:30.7452877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:30.7453381Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:30.7453701Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:30.7454026Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:53:30.7454294Z         
2025-03-14T04:53:30.7454703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:30.7455205Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:30.7455505Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:30.7456164Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:53:30.7456447Z         
2025-03-14T04:53:30.7456854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:30.7457447Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:30.7457722Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:53:30.7457992Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:53:30.7458238Z         
2025-03-14T04:53:30.7458853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:30.7459393Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:30.7459701Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:53:30.7459988Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:53:30.7460244Z         
2025-03-14T04:53:30.7460727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:30.7461262Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:30.7461602Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:53:30.7461850Z         
2025-03-14T04:53:30.7462255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:30.7462814Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:30.7463150Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:53:30.7463393Z         
2025-03-14T04:53:30.7463794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:30.7464447Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:30.7464782Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:53:30.7465037Z         
2025-03-14T04:53:30.7465444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:30.7465995Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:30.7466354Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:53:30.7466599Z         
2025-03-14T04:53:30.7467039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:30.7467588Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:30.7467857Z         
2025-03-14T04:53:30.7468290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:30.7468829Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:30.7469090Z         
2025-03-14T04:53:30.7469532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:30.7470133Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:30.7470471Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:53:30.7470813Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:30.7471168Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:53:30.7471482Z         
2025-03-14T04:53:30.7471930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:30.7472484Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:30.7472810Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:53:30.7473142Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:30.7473495Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:53:30.7473760Z         
2025-03-14T04:53:30.7474204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:30.7474699Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:30.7475037Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:30.7475377Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:53:30.7475631Z         
2025-03-14T04:53:30.7476051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:30.7476555Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:30.7476894Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:30.7477248Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:53:30.7477503Z         
2025-03-14T04:53:30.7477908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:30.7478372Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:30.7478640Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:30.7478879Z         
2025-03-14T04:53:30.7479276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:30.7479736Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:30.7479993Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:30.7480226Z         
2025-03-14T04:53:30.7480617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:30.7481095Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:30.7481389Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:30.7481881Z         
2025-03-14T04:53:30.7482280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:30.7482752Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:30.7483044Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:30.7483293Z         
2025-03-14T04:53:30.7483790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:30.7484375Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:30.7484712Z         
2025-03-14T04:53:30.7485132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:30.7485682Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:53:30.7485971Z         
2025-03-14T04:53:30.7486439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:53:30.7487051Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:53:30.7487340Z         
2025-03-14T04:53:30.7487906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:53:30.7488597Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:53:30.7488847Z         
2025-03-14T04:53:30.7489232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:30.7489719Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:53:30.7489976Z         
2025-03-14T04:53:30.7490503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:53:30.7491166Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:53:30.7491500Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:53:30.7491775Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:53:30.7492007Z         
2025-03-14T04:53:30.7492549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:53:30.7493220Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:53:30.7493718Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:53:30.7494068Z         
2025-03-14T04:53:30.7494612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:53:30.7495293Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:53:30.7495581Z         
2025-03-14T04:53:30.7495966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:53:30.7496473Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:53:30.7496746Z         
2025-03-14T04:53:30.7497257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:53:30.7497841Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:53:30.7498110Z         
2025-03-14T04:53:30.7498494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:53:30.7499037Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:53:30.7499298Z         
2025-03-14T04:53:30.7499756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:53:30.7500324Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:53:30.7500583Z         
2025-03-14T04:53:30.7501156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:53:30.7501817Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:53:30.7502127Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:30.7502453Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:53:30.7502793Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:53:30.7503048Z         
2025-03-14T04:53:30.7503503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:53:30.7504126Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:53:30.7504367Z         
2025-03-14T04:53:56.3319266Z 
2025-03-14T04:53:56.3320275Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:56.3322183Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:56.3323674Z         l_stack0_ = L_stack0_
2025-03-14T04:53:56.3324141Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:56.3324852Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:56.3325529Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:56.3326378Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:56.3327169Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3327806Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3328428Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3329399Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3329719Z         
2025-03-14T04:53:56.3330313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:53:56.3331352Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:53:56.3331686Z         
2025-03-14T04:53:56.3332097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:56.3333097Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:56.3333824Z         
2025-03-14T04:53:56.3334241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:56.3335271Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:56.3336023Z         
2025-03-14T04:53:56.3336402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3336869Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3337128Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:56.3337363Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:56.3337639Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3337903Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:56.3338152Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:56.3338374Z         
2025-03-14T04:53:56.3338746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:56.3339689Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3340412Z         
2025-03-14T04:53:56.3340877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:56.3341449Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:56.3341723Z         
2025-03-14T04:53:56.3342122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:56.3342646Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:56.3342923Z         
2025-03-14T04:53:56.3343321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:56.3343820Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:56.3344318Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3344659Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:56.3344934Z         
2025-03-14T04:53:56.3345358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:56.3345958Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:56.3346258Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:56.3346577Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:56.3346843Z         
2025-03-14T04:53:56.3347239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:56.3347738Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3348001Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:56.3348264Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:56.3348504Z         
2025-03-14T04:53:56.3348909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:56.3349420Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:56.3349709Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:56.3349972Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:56.3350215Z         
2025-03-14T04:53:56.3350652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:56.3351174Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:56.3351507Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:56.3351743Z         
2025-03-14T04:53:56.3352145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:56.3352653Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:56.3352981Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:56.3353219Z         
2025-03-14T04:53:56.3358771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:56.3359377Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:56.3359718Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:56.3359963Z         
2025-03-14T04:53:56.3360385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:56.3360938Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:56.3361292Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:56.3361526Z         
2025-03-14T04:53:56.3361958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:56.3362502Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:56.3362777Z         
2025-03-14T04:53:56.3363316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:56.3363851Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:56.3364168Z         
2025-03-14T04:53:56.3364611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:56.3365170Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:56.3365489Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:56.3365828Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:56.3366219Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:56.3366483Z         
2025-03-14T04:53:56.3366929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:56.3367480Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:56.3367803Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:56.3368136Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:56.3368495Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:56.3368751Z         
2025-03-14T04:53:56.3369183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:56.3369700Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:56.3370037Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:56.3370387Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:56.3370651Z         
2025-03-14T04:53:56.3371083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:56.3371601Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:56.3371941Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:56.3372446Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:56.3372814Z         
2025-03-14T04:53:56.3373310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:56.3373785Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:56.3374054Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:56.3374299Z         
2025-03-14T04:53:56.3374873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:56.3375373Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:56.3375639Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:56.3375879Z         
2025-03-14T04:53:56.3376279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:56.3376825Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:56.3377131Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:56.3377387Z         
2025-03-14T04:53:56.3377791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:56.3378529Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:56.3378835Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:56.3379091Z         
2025-03-14T04:53:56.3379535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:56.3380135Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:56.3380439Z         
2025-03-14T04:53:56.3380877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:56.3381723Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:56.3382036Z         
2025-03-14T04:53:56.3382495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:56.3383123Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:56.3383500Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:56.3383802Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:56.3384203Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:56.3384551Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:56.3384826Z         
2025-03-14T04:53:56.3385241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3385824Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3386184Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:56.3386445Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:56.3386829Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3387198Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:56.3387466Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:56.3387707Z         
2025-03-14T04:53:56.3388149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:56.3388734Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:56.3389045Z         
2025-03-14T04:53:56.3389509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:56.3390133Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:56.3390515Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:56.3390821Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:56.3391262Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:56.3391600Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:56.3391877Z         
2025-03-14T04:53:56.3392459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:56.3393257Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:56.3393616Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:56.3393972Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:56.3394328Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:56.3394628Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:56.3394872Z         
2025-03-14T04:53:56.3395309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:56.3395833Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:56.3396072Z         
2025-03-14T04:53:56.3396232Z 
2025-03-14T04:53:56.3396323Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:56.3397694Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:56.3399005Z         l_stack0_ = L_stack0_
2025-03-14T04:53:56.3399395Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:56.3399964Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:56.3400515Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:56.3401066Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:56.3401540Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3401937Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3402325Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3402714Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3403006Z         
2025-03-14T04:53:56.3403527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:53:56.3404154Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:53:56.3404418Z         
2025-03-14T04:53:56.3404855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:56.3405810Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:56.3406577Z         
2025-03-14T04:53:56.3406992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:56.3408014Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:56.3408765Z         
2025-03-14T04:53:56.3409145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3409620Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3409879Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:56.3410121Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:56.3410405Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3410674Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:56.3410924Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:56.3411151Z         
2025-03-14T04:53:56.3411531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:56.3412462Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3413183Z         
2025-03-14T04:53:56.3413653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:56.3414234Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:56.3414512Z         
2025-03-14T04:53:56.3414912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:56.3415446Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:56.3415733Z         
2025-03-14T04:53:56.3416138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:56.3416650Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:56.3416964Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3417294Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:56.3417568Z         
2025-03-14T04:53:56.3417990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:56.3418518Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:56.3418871Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:56.3419209Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:56.3419487Z         
2025-03-14T04:53:56.3419911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:56.3420507Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3420795Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:56.3421074Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:56.3421407Z         
2025-03-14T04:53:56.3422009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:56.3422779Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:56.3423090Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:56.3423370Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:56.3423627Z         
2025-03-14T04:53:56.3424061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:56.3424682Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:56.3425045Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:56.3425372Z         
2025-03-14T04:53:56.3425843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:56.3426386Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:56.3426724Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:56.3426966Z         
2025-03-14T04:53:56.3427366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:56.3427882Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:56.3428217Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:56.3428459Z         
2025-03-14T04:53:56.3428861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:56.3429409Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:56.3429768Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:56.3430007Z         
2025-03-14T04:53:56.3430441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:56.3430982Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:56.3431244Z         
2025-03-14T04:53:56.3431673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:56.3432207Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:56.3432469Z         
2025-03-14T04:53:56.3432914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:56.3433525Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:56.3433853Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:56.3434195Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:56.3434589Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:56.3434858Z         
2025-03-14T04:53:56.3435306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:56.3435861Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:56.3436191Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:56.3436511Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:56.3436850Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:56.3437108Z         
2025-03-14T04:53:56.3437546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:56.3438053Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:56.3438378Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:56.3438722Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:56.3438977Z         
2025-03-14T04:53:56.3439403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:56.3439915Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:56.3440276Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:56.3440628Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:56.3440889Z         
2025-03-14T04:53:56.3441293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:56.3441755Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:56.3442019Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:56.3442256Z         
2025-03-14T04:53:56.3442651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:56.3443112Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:56.3443370Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:56.3443604Z         
2025-03-14T04:53:56.3443997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:56.3444481Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:56.3444770Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:56.3445021Z         
2025-03-14T04:53:56.3445409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:56.3445879Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:56.3446212Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:56.3446463Z         
2025-03-14T04:53:56.3446899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:56.3447525Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:56.3447820Z         
2025-03-14T04:53:56.3448242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:56.3448802Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:56.3449091Z         
2025-03-14T04:53:56.3449546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:56.3450162Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:56.3450534Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:56.3450838Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:56.3451146Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:56.3451464Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:56.3451718Z         
2025-03-14T04:53:56.3452098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3452658Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3453009Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:56.3453253Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:56.3453615Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3453969Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:56.3454220Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:56.3454443Z         
2025-03-14T04:53:56.3454865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:56.3455424Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:56.3455715Z         
2025-03-14T04:53:56.3456162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:56.3456764Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:56.3457124Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:56.3457422Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:56.3457729Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:56.3458050Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:56.3458312Z         
2025-03-14T04:53:56.3458868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:56.3459610Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:56.3459960Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:56.3460301Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:56.3460644Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:56.3460973Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:56.3461219Z         
2025-03-14T04:53:56.3461658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:56.3462180Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:56.3462417Z         
2025-03-14T04:53:56.3462564Z 
2025-03-14T04:53:56.3462660Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:56.3464049Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:56.3465557Z         l_stack0_ = L_stack0_
2025-03-14T04:53:56.3465957Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:56.3466536Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:56.3467109Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:56.3467679Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:56.3468177Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3468597Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3469011Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3469452Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3469753Z         
2025-03-14T04:53:56.3470302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:53:56.3470957Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:53:56.3471235Z         
2025-03-14T04:53:56.3471646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:56.3472642Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:56.3473374Z         
2025-03-14T04:53:56.3473846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:56.3474875Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:56.3475674Z         
2025-03-14T04:53:56.3476074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3476746Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3477034Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:56.3477277Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:56.3477564Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3477832Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:56.3478091Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:56.3478321Z         
2025-03-14T04:53:56.3478859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:56.3479896Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3480860Z         
2025-03-14T04:53:56.3481337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:56.3482112Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:56.3482396Z         
2025-03-14T04:53:56.3482810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:56.3483351Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:56.3483638Z         
2025-03-14T04:53:56.3484054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:56.3484565Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:56.3484872Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3485204Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:56.3485475Z         
2025-03-14T04:53:56.3485892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:56.3486405Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:56.3486701Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:56.3487017Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:56.3487281Z         
2025-03-14T04:53:56.3487677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:56.3488165Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3488426Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:56.3488781Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:56.3489029Z         
2025-03-14T04:53:56.3489430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:56.3490017Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:56.3490308Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:56.3490571Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:56.3490813Z         
2025-03-14T04:53:56.3491227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:56.3491738Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:56.3492069Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:56.3492307Z         
2025-03-14T04:53:56.3492694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:56.3493197Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:56.3493519Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:56.3493753Z         
2025-03-14T04:53:56.3494141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:56.3494641Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:56.3494970Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:56.3495212Z         
2025-03-14T04:53:56.3495602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:56.3496133Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:56.3496480Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:56.3496717Z         
2025-03-14T04:53:56.3497143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:56.3497675Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:56.3497931Z         
2025-03-14T04:53:56.3498350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:56.3498863Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:56.3499117Z         
2025-03-14T04:53:56.3499545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:56.3500093Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:56.3500417Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:56.3500748Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:56.3501091Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:56.3501348Z         
2025-03-14T04:53:56.3501818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:56.3502355Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:56.3502669Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:56.3503032Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:56.3503372Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:56.3503630Z         
2025-03-14T04:53:56.3504064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:56.3504667Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:56.3505017Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:56.3505385Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:56.3505638Z         
2025-03-14T04:53:56.3506058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:56.3506566Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:56.3506896Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:56.3507243Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:56.3507494Z         
2025-03-14T04:53:56.3507907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:56.3508375Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:56.3508635Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:56.3508869Z         
2025-03-14T04:53:56.3509264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:56.3509722Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:56.3509984Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:56.3510217Z         
2025-03-14T04:53:56.3510611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:56.3511082Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:56.3511376Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:56.3511625Z         
2025-03-14T04:53:56.3512017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:56.3512489Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:56.3512780Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:56.3513022Z         
2025-03-14T04:53:56.3513451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:56.3514027Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:56.3514317Z         
2025-03-14T04:53:56.3514803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:56.3515363Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:56.3515647Z         
2025-03-14T04:53:56.3516089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:56.3516751Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:56.3517117Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:56.3517408Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:56.3517720Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:56.3518034Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:56.3518299Z         
2025-03-14T04:53:56.3518681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3519241Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3519594Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:56.3519842Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:56.3520212Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3520574Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:56.3520825Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:56.3521048Z         
2025-03-14T04:53:56.3521479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:56.3522018Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:56.3522302Z         
2025-03-14T04:53:56.3522735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:56.3523321Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:56.3523671Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:56.3523953Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:56.3524244Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:56.3524553Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:56.3524810Z         
2025-03-14T04:53:56.3525346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:56.3526034Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:56.3526379Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:56.3526720Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:56.3527069Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:56.3527364Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:56.3527608Z         
2025-03-14T04:53:56.3528088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:56.3528609Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:56.3528847Z         
2025-03-14T04:53:56.3529023Z 
2025-03-14T04:53:56.3529115Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:56.3530470Z     def forward(self, L_stack0_: "f32[3261, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:56.3531773Z         l_stack0_ = L_stack0_
2025-03-14T04:53:56.3532158Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:53:56.3532714Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:53:56.3533267Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:53:56.3533811Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:53:56.3534282Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3534683Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3535084Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3535463Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:56.3535754Z         
2025-03-14T04:53:56.3536280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T04:53:56.3536893Z         mean: "f32[3261, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T04:53:56.3537161Z         
2025-03-14T04:53:56.3537554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:53:56.3538515Z         scores: "f32[3261, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:53:56.3539225Z         
2025-03-14T04:53:56.3539633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:53:56.3540630Z         proposal_deltas: "f32[3261, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:53:56.3541373Z         
2025-03-14T04:53:56.3541787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3542250Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3542501Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:56.3542771Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:56.3543046Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:56.3543308Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:56.3543554Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:56.3543774Z         
2025-03-14T04:53:56.3544205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:56.3545168Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3545905Z         
2025-03-14T04:53:56.3546380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:56.3546975Z         deltas: "f32[3261, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:53:56.3547254Z         
2025-03-14T04:53:56.3547668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:56.3548189Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:56.3548463Z         
2025-03-14T04:53:56.3548870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:56.3549372Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:56.3549679Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3550001Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:56.3550263Z         
2025-03-14T04:53:56.3550673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:56.3551163Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:56.3551459Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:56.3551776Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:56.3552038Z         
2025-03-14T04:53:56.3552436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:56.3552918Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:56.3553180Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:56.3553437Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:56.3553676Z         
2025-03-14T04:53:56.3554078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:56.3554588Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:56.3554871Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:56.3555181Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:56.3555428Z         
2025-03-14T04:53:56.3555836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:56.3556401Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:56.3556730Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:56.3556968Z         
2025-03-14T04:53:56.3557358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:56.3557867Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:56.3558194Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:56.3558429Z         
2025-03-14T04:53:56.3558813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:56.3559312Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:56.3559636Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:56.3559868Z         
2025-03-14T04:53:56.3560257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:56.3560790Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:56.3561133Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:56.3561365Z         
2025-03-14T04:53:56.3561786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:56.3562313Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:56.3562571Z         
2025-03-14T04:53:56.3562988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:56.3563509Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:56.3563761Z         
2025-03-14T04:53:56.3564190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:56.3564724Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:56.3565042Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:56.3565372Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:56.3565708Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:56.3565969Z         
2025-03-14T04:53:56.3566403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:56.3566932Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:56.3567243Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:56.3567566Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:56.3567948Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:56.3568211Z         
2025-03-14T04:53:56.3568632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:56.3569135Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:56.3569503Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:56.3569844Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:56.3570097Z         
2025-03-14T04:53:56.3570515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:56.3571019Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:56.3571350Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:56.3571700Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:56.3571955Z         
2025-03-14T04:53:56.3572356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:56.3572827Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:56.3573091Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:56.3573333Z         
2025-03-14T04:53:56.3573731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:56.3574187Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:56.3574446Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:56.3574683Z         
2025-03-14T04:53:56.3575076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:56.3575555Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:56.3575851Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:56.3576111Z         
2025-03-14T04:53:56.3576508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:56.3576984Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:56.3577273Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:56.3577517Z         
2025-03-14T04:53:56.3577960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:56.3578548Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:56.3578845Z         
2025-03-14T04:53:56.3579273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:56.3579838Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:56.3580131Z         
2025-03-14T04:53:56.3580588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:56.3581399Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:56.3581967Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:56.3582265Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:56.3582584Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:56.3582997Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:56.3583272Z         
2025-03-14T04:53:56.3583669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:56.3584299Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3584654Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:56.3584899Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:56.3585276Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:56.3585641Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:56.3585901Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:56.3586130Z         
2025-03-14T04:53:56.3586563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:56.3587133Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:53:56.3587430Z         
2025-03-14T04:53:56.3587881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:56.3588490Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:56.3588865Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:56.3589162Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:56.3589472Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:56.3589799Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:56.3590069Z         
2025-03-14T04:53:56.3590627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:56.3591329Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:56.3591681Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:56.3592032Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:56.3592381Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:56.3592681Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:56.3592933Z         
2025-03-14T04:53:56.3593384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:56.3593912Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:56.3594151Z         
2025-03-14T04:53:57.9740992Z 
2025-03-14T04:53:57.9741626Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:57.9743070Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:57.9743957Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:57.9744485Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:57.9744826Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9745254Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9745679Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9746121Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9746536Z         
2025-03-14T04:53:57.9747053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9747549Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9747817Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:57.9748060Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:57.9748359Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9748665Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:57.9749011Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:57.9749245Z         
2025-03-14T04:53:57.9749634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:57.9750612Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9751342Z         
2025-03-14T04:53:57.9751947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:57.9752554Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:57.9752838Z         
2025-03-14T04:53:57.9753249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:57.9753791Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:57.9754081Z         
2025-03-14T04:53:57.9754503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:57.9755022Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:57.9755340Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9755672Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:57.9755945Z         
2025-03-14T04:53:57.9756366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:57.9756880Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:57.9757186Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:57.9757512Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:57.9757785Z         
2025-03-14T04:53:57.9758259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:57.9758768Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9759120Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:57.9759386Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:57.9759631Z         
2025-03-14T04:53:57.9760039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:57.9760559Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:57.9760858Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:57.9761124Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:57.9761373Z         
2025-03-14T04:53:57.9761817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:57.9762337Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:57.9762676Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:57.9762914Z         
2025-03-14T04:53:57.9763311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:57.9763823Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:57.9764154Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:57.9764397Z         
2025-03-14T04:53:57.9764797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:57.9765310Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:57.9765638Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:57.9765882Z         
2025-03-14T04:53:57.9766279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:57.9766825Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:57.9767177Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:57.9767415Z         
2025-03-14T04:53:57.9767863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:57.9768390Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:57.9768647Z         
2025-03-14T04:53:57.9769062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:57.9769585Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:57.9769837Z         
2025-03-14T04:53:57.9770264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:57.9770801Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:57.9771118Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:57.9771485Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:57.9771838Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:57.9772102Z         
2025-03-14T04:53:57.9772577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:57.9773123Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:57.9773438Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:57.9773765Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:57.9774117Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:57.9774375Z         
2025-03-14T04:53:57.9774807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:57.9775312Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:57.9775642Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:57.9775982Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:57.9776234Z         
2025-03-14T04:53:57.9776654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:57.9777155Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:57.9777485Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:57.9777837Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:57.9778090Z         
2025-03-14T04:53:57.9778511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:57.9778981Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:57.9779247Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:57.9779484Z         
2025-03-14T04:53:57.9779876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:57.9780331Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:57.9780592Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:57.9780826Z         
2025-03-14T04:53:57.9781218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:57.9781986Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:57.9782288Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:57.9782549Z         
2025-03-14T04:53:57.9782941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:57.9783468Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:57.9783761Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:57.9784014Z         
2025-03-14T04:53:57.9784620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:57.9785239Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:57.9785557Z         
2025-03-14T04:53:57.9785988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:57.9786628Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:57.9786930Z         
2025-03-14T04:53:57.9787392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:57.9788032Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:57.9788417Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:57.9788724Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:57.9789038Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:57.9789373Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:57.9789649Z         
2025-03-14T04:53:57.9790049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9790631Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9790996Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:57.9791254Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:57.9791758Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9792261Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:57.9792521Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:57.9792754Z         
2025-03-14T04:53:57.9793197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:57.9793846Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:57.9794349Z         
2025-03-14T04:53:57.9795006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:57.9795830Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:57.9796221Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:57.9796534Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:57.9796856Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:57.9797199Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:57.9797477Z         
2025-03-14T04:53:57.9798070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:57.9798844Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:57.9799380Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:57.9799934Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:57.9800312Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:57.9800632Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:57.9800897Z         
2025-03-14T04:53:57.9801421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:57.9801957Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:57.9802203Z         
2025-03-14T04:53:57.9802341Z 
2025-03-14T04:53:57.9802438Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:57.9803264Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:57.9804064Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:57.9804305Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:57.9804634Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9805048Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9805453Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9805859Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9806158Z         
2025-03-14T04:53:57.9806550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9807023Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9807282Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:57.9807518Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:57.9807798Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9808070Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:57.9808319Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:57.9808547Z         
2025-03-14T04:53:57.9808929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:57.9809882Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9810605Z         
2025-03-14T04:53:57.9811078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:57.9811665Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:57.9811945Z         
2025-03-14T04:53:57.9812362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:57.9812880Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:57.9813158Z         
2025-03-14T04:53:57.9813618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:57.9814116Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:57.9814424Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9814740Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:57.9815035Z         
2025-03-14T04:53:57.9815443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:57.9815935Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:57.9816229Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:57.9816542Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:57.9816804Z         
2025-03-14T04:53:57.9817201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:57.9817679Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9817933Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:57.9818190Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:57.9818419Z         
2025-03-14T04:53:57.9818818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:57.9819328Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:57.9819612Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:57.9819876Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:57.9820120Z         
2025-03-14T04:53:57.9820534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:57.9821047Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:57.9821377Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:57.9821617Z         
2025-03-14T04:53:57.9822005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:57.9822513Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:57.9822834Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:57.9823068Z         
2025-03-14T04:53:57.9823460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:57.9823966Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:57.9824404Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:57.9824661Z         
2025-03-14T04:53:57.9825077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:57.9825638Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:57.9825995Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:57.9826232Z         
2025-03-14T04:53:57.9826663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:57.9827238Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:57.9827503Z         
2025-03-14T04:53:57.9827922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:57.9828487Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:57.9828757Z         
2025-03-14T04:53:57.9829207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:57.9829752Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:57.9830083Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:57.9830427Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:57.9830786Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:57.9831057Z         
2025-03-14T04:53:57.9831516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:57.9832076Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:57.9832404Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:57.9832841Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:57.9833288Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:57.9833551Z         
2025-03-14T04:53:57.9833980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:57.9834485Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:57.9834822Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:57.9835323Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:57.9835591Z         
2025-03-14T04:53:57.9836011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:57.9836514Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:57.9836841Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:57.9837261Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:57.9837557Z         
2025-03-14T04:53:57.9837958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:57.9838428Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:57.9838687Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:57.9838925Z         
2025-03-14T04:53:57.9839321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:57.9839784Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:57.9840044Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:57.9840279Z         
2025-03-14T04:53:57.9840715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:57.9841188Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:57.9841480Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:57.9841768Z         
2025-03-14T04:53:57.9842160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:57.9842633Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:57.9842923Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:57.9843172Z         
2025-03-14T04:53:57.9843608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:57.9844187Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:57.9844477Z         
2025-03-14T04:53:57.9844897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:57.9845448Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:57.9845735Z         
2025-03-14T04:53:57.9846179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:57.9846791Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:57.9847158Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:57.9847455Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:57.9847759Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:57.9848077Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:57.9848346Z         
2025-03-14T04:53:57.9848727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9849278Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9849627Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:57.9849871Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:57.9850261Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9850618Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:57.9850865Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:57.9851085Z         
2025-03-14T04:53:57.9851504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:57.9852103Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:57.9852426Z         
2025-03-14T04:53:57.9852862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:57.9853470Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:57.9853830Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:57.9855231Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:57.9855566Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:57.9855888Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:57.9856189Z         
2025-03-14T04:53:57.9856746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:57.9857428Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:57.9857772Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:57.9858106Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:57.9858448Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:57.9858744Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:57.9858984Z         
2025-03-14T04:53:57.9859424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:57.9859945Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:57.9860177Z         
2025-03-14T04:53:57.9860325Z 
2025-03-14T04:53:57.9860426Z class GraphModule(torch.nn.Module):
2025-03-14T04:53:57.9861228Z     def forward(self, L_predictions_0_: "f32[3261, 81][81, 1]cpu", L_predictions_1_: "f32[3261, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1261 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1261 - s0, 4][4, 1]cpu"):
2025-03-14T04:53:57.9862018Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:53:57.9862247Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:53:57.9862564Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9862964Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9863363Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9863756Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:53:57.9864064Z         
2025-03-14T04:53:57.9864622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9865135Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9865402Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:53:57.9865647Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:53:57.9865932Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:53:57.9866199Z         getitem_2: "Sym(1261 - s0)" = size_1[0]
2025-03-14T04:53:57.9866456Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:53:57.9866691Z         
2025-03-14T04:53:57.9867085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:53:57.9868146Z         proposal_boxes: "f32[3261, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9868900Z         
2025-03-14T04:53:57.9869386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:53:57.9870034Z         deltas: "f32[3261, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:53:57.9870328Z         
2025-03-14T04:53:57.9870747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:53:57.9871298Z         boxes: "f32[3261, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:53:57.9871593Z         
2025-03-14T04:53:57.9872016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:53:57.9872537Z         getitem_4: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:53:57.9872856Z         getitem_5: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9873190Z         widths: "f32[3261][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:53:57.9873466Z         
2025-03-14T04:53:57.9873889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:53:57.9874409Z         getitem_6: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:53:57.9874713Z         getitem_7: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:53:57.9875042Z         heights: "f32[3261][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:53:57.9875318Z         
2025-03-14T04:53:57.9875738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:53:57.9876244Z         getitem_8: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:53:57.9876516Z         mul: "f32[3261][1]cpu" = 0.5 * widths
2025-03-14T04:53:57.9876778Z         ctr_x: "f32[3261][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:53:57.9877016Z         
2025-03-14T04:53:57.9877412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:53:57.9877917Z         getitem_9: "f32[3261][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:53:57.9878199Z         mul_1: "f32[3261][1]cpu" = 0.5 * heights
2025-03-14T04:53:57.9878462Z         ctr_y: "f32[3261][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:53:57.9878697Z         
2025-03-14T04:53:57.9879110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:53:57.9879615Z         getitem_10: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:53:57.9879938Z         dx: "f32[3261, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:53:57.9880176Z         
2025-03-14T04:53:57.9880561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:53:57.9881063Z         getitem_11: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:53:57.9881381Z         dy: "f32[3261, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:53:57.9881849Z         
2025-03-14T04:53:57.9882251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:53:57.9882868Z         getitem_12: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:53:57.9883194Z         dw: "f32[3261, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:53:57.9883428Z         
2025-03-14T04:53:57.9883886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:53:57.9884427Z         getitem_13: "f32[3261, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:53:57.9884778Z         dh: "f32[3261, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:53:57.9885019Z         
2025-03-14T04:53:57.9885457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:53:57.9886022Z         dw_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:53:57.9886282Z         
2025-03-14T04:53:57.9886723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:53:57.9887266Z         dh_1: "f32[3261, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:53:57.9887531Z         
2025-03-14T04:53:57.9887987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:53:57.9888539Z         getitem_14: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:53:57.9888866Z         mul_2: "f32[3261, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:53:57.9889206Z         getitem_15: "f32[3261, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:53:57.9889564Z         pred_ctr_x: "f32[3261, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:53:57.9889833Z         
2025-03-14T04:53:57.9890293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:53:57.9890824Z         getitem_16: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:53:57.9891132Z         mul_3: "f32[3261, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:53:57.9891447Z         getitem_17: "f32[3261, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:53:57.9891779Z         pred_ctr_y: "f32[3261, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:53:57.9892028Z         
2025-03-14T04:53:57.9892444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:53:57.9892937Z         exp: "f32[3261, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:53:57.9893253Z         getitem_18: "f32[3261, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:53:57.9893592Z         pred_w: "f32[3261, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:53:57.9893841Z         
2025-03-14T04:53:57.9894260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:53:57.9894762Z         exp_1: "f32[3261, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:53:57.9895091Z         getitem_19: "f32[3261, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:53:57.9895441Z         pred_h: "f32[3261, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:53:57.9895734Z         
2025-03-14T04:53:57.9896146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:53:57.9896611Z         mul_6: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:53:57.9896921Z         x1: "f32[3261, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:53:57.9897163Z         
2025-03-14T04:53:57.9897564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:53:57.9898032Z         mul_7: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:53:57.9898304Z         y1: "f32[3261, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:53:57.9898545Z         
2025-03-14T04:53:57.9898946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:53:57.9899429Z         mul_8: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:53:57.9899726Z         x2: "f32[3261, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:53:57.9899982Z         
2025-03-14T04:53:57.9900380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:53:57.9900855Z         mul_9: "f32[3261, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:53:57.9901147Z         y2: "f32[3261, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:53:57.9901390Z         
2025-03-14T04:53:57.9901832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:53:57.9902423Z         pred_boxes: "f32[3261, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:53:57.9902721Z         
2025-03-14T04:53:57.9903146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:53:57.9903708Z         predict_boxes: "f32[3261, 320][320, 1]cpu" = pred_boxes.reshape((3261, 320));  pred_boxes = None
2025-03-14T04:53:57.9904009Z         
2025-03-14T04:53:57.9904540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:53:57.9905165Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:53:57.9905539Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:53:57.9905844Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:53:57.9906145Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:53:57.9906459Z         getitem_23: "f32[1261 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:53:57.9906728Z         
2025-03-14T04:53:57.9907103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:53:57.9907653Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9907996Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:53:57.9908240Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:53:57.9908598Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:53:57.9908950Z         getitem_26: "Sym(1261 - s0)" = size_3[0]
2025-03-14T04:53:57.9909238Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:53:57.9909459Z         
2025-03-14T04:53:57.9909881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:53:57.9910531Z         probs: "f32[3261, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:53:57.9910862Z         
2025-03-14T04:53:57.9911304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:53:57.9911899Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:53:57.9912257Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:53:57.9912549Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:53:57.9912852Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:53:57.9913175Z         getitem_31: "f32[1261 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:53:57.9913445Z         
2025-03-14T04:53:57.9914032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:53:57.9914750Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:53:57.9915123Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:53:57.9915469Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:53:57.9915832Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:53:57.9916134Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:53:57.9916381Z         
2025-03-14T04:53:57.9916879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:53:57.9917462Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:53:57.9917704Z         
2025-03-14T04:54:00.3072810Z 
2025-03-14T04:54:00.3073614Z class GraphModule(torch.nn.Module):
2025-03-14T04:54:00.3074289Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:54:00.3075497Z         l_scores_0_ = L_scores_0_
2025-03-14T04:54:00.3075812Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:54:00.3076021Z         
2025-03-14T04:54:00.3076712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:54:00.3077552Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:54:00.3077912Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:54:00.3078258Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:54:00.3078598Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:54:00.3078905Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:54:00.3079157Z         
2025-03-14T04:54:00.3079620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:54:00.3080560Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:54:00.3080820Z         
2025-03-14T04:54:00.3080914Z 
2025-03-14T04:54:00.3081008Z class GraphModule(torch.nn.Module):
2025-03-14T04:54:00.3081335Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:54:00.3081999Z         l_scores_0_ = L_scores_0_
2025-03-14T04:54:00.3082223Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:54:00.3082429Z         
2025-03-14T04:54:00.3083016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:54:00.3083718Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:54:00.3084065Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:54:00.3084411Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:54:00.3084739Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:54:00.3085048Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:54:00.3085307Z         
2025-03-14T04:54:00.3085767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:54:00.3086305Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:54:00.3086553Z         
2025-03-14T04:54:34.8237099Z Compilation time (from dynamo_timed): 79.731094732
2025-03-14T04:54:34.8241782Z pass
2025-03-14T04:54:34.8246228Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:54:34.8252022Z TIMING: entire_frame_compile:79.73109 gc:0.05234 _recursive_pre_grad_passes:0.03438 _recursive_joint_graph_passes:0.27464 _recursive_post_grad_passes:0.28065 async_compile.wait:37.753 code_gen:50.77651 inductor_compile:55.4209 backend_compile:64.30605 total_wall_time:79.73109
2025-03-14T04:54:34.8253861Z STATS: call_* op count: 777 | FakeTensorMode.__torch_dispatch__:33338 | FakeTensor.__torch_dispatch__:3861 | ProxyTorchDispatchMode.__torch_dispatch__:12075 | attempt fast:91 | slow no contiguity match:44 | fast is_contiguous:47
2025-03-14T04:54:34.8256254Z Dynamo produced 53 graphs covering 777 ops with 42 graph breaks (6 unique)
2025-03-14T04:54:40.9945025Z 
2025-03-14T04:54:51.5518758Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:54:51.5519179Z loading model: 0it [00:10, ?it/s]
2025-03-14T04:54:51.5531969Z cpu  eval  detectron2_fasterrcnn_r_101_dc5    
2025-03-14T04:55:08.3972266Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_dc5. Setting accuracy check to cosine
2025-03-14T04:55:08.4079895Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:55:19.6892867Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:55:31.4493864Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:55:43.5537280Z 
2025-03-14T04:55:43.5539653Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:43.5649499Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", 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"f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:55:43.5749101Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:55:43.5749756Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5750530Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5751375Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5752194Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5752994Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5753769Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5754617Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5755508Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5756390Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5757212Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5758007Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5758791Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5759516Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5760229Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5760920Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5761579Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5762265Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5762985Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5763756Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5764430Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5765122Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.5765827Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5766576Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5767309Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.5768017Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.5768676Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5769357Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5770099Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5770818Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5771499Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5772145Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5772822Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5773561Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5774289Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5774985Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5775661Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5776354Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5777144Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5777872Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5778570Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5779256Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5779981Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5780717Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5781528Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5782265Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5782964Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5783666Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5784451Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5785182Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5785910Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5786588Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5787271Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5788007Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5788720Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5789402Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5790053Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5790730Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5791528Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5792248Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5792976Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5793635Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5794322Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5795058Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5795770Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5796465Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5797119Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5797797Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5798527Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5799240Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5799927Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5800597Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.5801311Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5802069Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5802874Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.5803598Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.5804306Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5805105Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5805940Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5806769Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5807526Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5808218Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5808973Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5809784Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5810532Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5811202Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5811844Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5812539Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5813282Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5814004Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5814697Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5815351Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5816024Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5816742Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5817438Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5818113Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5818754Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5819461Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5820190Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5820923Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5821593Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5822229Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5822901Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5823614Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5824388Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5825122Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5825814Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5826499Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5827237Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5827954Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5828642Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5829300Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5829984Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5830719Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5831436Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5832130Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5832821Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5833508Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5834275Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5834994Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5835677Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5836335Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5837014Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5837752Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5838464Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5839162Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5839836Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5840521Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5841269Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5841977Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5842661Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5843320Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5844002Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5844735Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5845447Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5846187Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5846847Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.5847548Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5848338Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5849076Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.5849786Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.5850451Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5851132Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5851868Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5852578Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5853270Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5853919Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5854602Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5855329Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5856050Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5856720Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5857353Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5858019Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5858737Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5859430Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5860158Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5860820Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5861528Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5862258Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5862974Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5863663Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5864393Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5865132Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5865884Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5866606Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5867290Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5867945Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5868638Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5869367Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5870082Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5870767Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5871415Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5872094Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5872818Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5873563Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5874257Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5874955Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5875640Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5876382Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5877105Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5877801Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5878467Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5879150Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5879888Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5880614Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5881325Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5882164Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5882849Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5883632Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5884360Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5885038Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5885688Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5886385Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5887139Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5887974Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5888669Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5889382Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5890059Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5890777Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5891471Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5892137Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5892777Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5893445Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5894162Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5894850Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5895513Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5896147Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5896823Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5897557Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5898264Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5898975Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5899632Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5900334Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5901116Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5901823Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5902536Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5903217Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5903938Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5904795Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5905593Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5906356Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5907057Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5907791Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5908587Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5909339Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5910065Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5910759Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5911487Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5912262Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5913009Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5913736Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5914434Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5915124Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5915911Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5916630Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5917347Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5918006Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5918686Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5919419Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5920121Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5920808Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5921454Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5922139Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5922871Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5923584Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5924264Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5924908Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5925587Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5926314Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5927019Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5927698Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5928342Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5929053Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5929802Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5930541Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5931240Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5931899Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5932590Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5933328Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5934047Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5934736Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5935412Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5936117Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5936834Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5937535Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5938204Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5938853Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5939538Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5940271Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5940990Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5941679Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5942336Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5943057Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5943791Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5944599Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5945375Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5946247Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5946979Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5947720Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5948448Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5949144Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5949801Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5950488Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5951229Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5951945Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5952633Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5953289Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5953972Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5954710Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5955422Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5956115Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5956808Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5957490Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5958272Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5958983Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5959671Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5960334Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5961014Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5961752Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5962476Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5963170Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5963829Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5964508Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5965248Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5965963Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5966655Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5967310Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5967998Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5968735Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5969449Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5970175Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5970841Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5971558Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5972297Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5973010Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5973703Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5974357Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5975049Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5975786Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5976506Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5977206Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5977867Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5978562Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5979296Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5980005Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5980695Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5981365Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5982268Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5983076Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5983919Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5984721Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5985546Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5986317Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5987134Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5987945Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5988719Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.5989411Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:55:43.5990156Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5990939Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5991695Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.5992423Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.5993124Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:55:43.5993866Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5994650Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5995408Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.5996131Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.5996828Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:55:43.5997556Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.5998331Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.5999116Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.5999809Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6000494Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6001179Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6001937Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6002687Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6003408Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6004104Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6004824Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6005606Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6006356Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6007081Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6007772Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6008499Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6009275Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6009989Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6010678Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6011340Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6012023Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6012806Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6013527Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6014258Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6014922Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6015625Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6016424Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6017142Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6017839Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6018503Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6019270Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6020068Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6020843Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6021585Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6022300Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6023042Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6023849Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6024729Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6025545Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6026278Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6027058Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6027855Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6028664Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6029406Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6030108Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6030844Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6031625Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6032385Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6033114Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6033818Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6034567Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6035349Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6036108Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6036831Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6037488Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6038180Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6038922Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6039644Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6040334Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6041039Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6041724Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6042463Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6043212Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6043911Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6044581Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6045269Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6046013Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6046731Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6047425Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6048089Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6048781Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6049523Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6050243Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6050936Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6051601Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6052290Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6053040Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6053762Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6054482Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6055224Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6055971Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6056786Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6057539Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6058267Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6058977Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6059719Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6060523Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6061319Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6062111Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6062934Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6064017Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6065057Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6065874Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6066699Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6067494Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6068538Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6069407Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6070229Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6071851Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6072567Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6073308Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6074084Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6074822Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6075535Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6076207Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6076916Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6077675Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6078409Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6079118Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6079791Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6080494Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6081244Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6082096Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6082814Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6083484Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6084190Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6084947Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6085751Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6086466Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6087194Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6087903Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6088662Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6089405Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6090120Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6090797Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6091499Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6092256Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6092988Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6093696Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6094370Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6095073Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6095828Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6096575Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6097296Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6097984Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6098698Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6099619Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6100571Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6101419Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6102276Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.6103147Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6104266Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6105215Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.6106020Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.6106775Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6107533Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6108341Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6109169Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6109995Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6110683Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6111415Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6112205Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6112990Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6113870Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6114728Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6115649Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6116558Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6117310Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6118064Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6118749Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6119566Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6120527Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6121469Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6122301Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6123000Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6123723Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6124528Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6125358Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6126150Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6126823Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6127526Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6128280Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6129016Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6129724Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6130465Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:55:43.6131247Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:55:43.6131957Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:55:43.6132714Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:55:43.6133556Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:55:43.6134347Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:55:43.6135124Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:55:43.6135614Z         
2025-03-14T04:55:43.6136030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6136867Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6137504Z         
2025-03-14T04:55:43.6137889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6139779Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6159158Z         
2025-03-14T04:55:43.6159814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:55:43.6160354Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:55:43.6160673Z         
2025-03-14T04:55:43.6161166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:55:43.6161870Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:55:43.6162258Z         
2025-03-14T04:55:43.6162627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6163410Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6164000Z         
2025-03-14T04:55:43.6164523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6166419Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6168119Z         
2025-03-14T04:55:43.6168504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6168996Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:55:43.6169263Z         
2025-03-14T04:55:43.6169611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6170355Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6170903Z         
2025-03-14T04:55:43.6171267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6173110Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6174771Z         
2025-03-14T04:55:43.6175155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6175637Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:55:43.6175899Z         
2025-03-14T04:55:43.6176245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6176999Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6177558Z         
2025-03-14T04:55:43.6177910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6179850Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6181856Z         
2025-03-14T04:55:43.6182252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6183088Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.6183717Z         
2025-03-14T04:55:43.6184215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6186433Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6188226Z         
2025-03-14T04:55:43.6188617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6189135Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:55:43.6189415Z         
2025-03-14T04:55:43.6189811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6190339Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:55:43.6190628Z         
2025-03-14T04:55:43.6190988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6191777Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6192345Z         
2025-03-14T04:55:43.6192721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6194763Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6196567Z         
2025-03-14T04:55:43.6196957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6197484Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:55:43.6197766Z         
2025-03-14T04:55:43.6198126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6198879Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6199434Z         
2025-03-14T04:55:43.6199783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6201679Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6203414Z         
2025-03-14T04:55:43.6203807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6204299Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:55:43.6204560Z         
2025-03-14T04:55:43.6204905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6205645Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6206200Z         
2025-03-14T04:55:43.6206554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6208462Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6210113Z         
2025-03-14T04:55:43.6210481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6210975Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:55:43.6211248Z         
2025-03-14T04:55:43.6211625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6212118Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:55:43.6212388Z         
2025-03-14T04:55:43.6212730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6213458Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6213999Z         
2025-03-14T04:55:43.6214353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6216198Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6217826Z         
2025-03-14T04:55:43.6218199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6218682Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:55:43.6218957Z         
2025-03-14T04:55:43.6219310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6220093Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6220678Z         
2025-03-14T04:55:43.6221048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6223021Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6224894Z         
2025-03-14T04:55:43.6225325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6225873Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:55:43.6226153Z         
2025-03-14T04:55:43.6226509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6227288Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6227874Z         
2025-03-14T04:55:43.6228246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6230198Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6231925Z         
2025-03-14T04:55:43.6232315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6232834Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:55:43.6233128Z         
2025-03-14T04:55:43.6233521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6234042Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:55:43.6234344Z         
2025-03-14T04:55:43.6234682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6235410Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6235947Z         
2025-03-14T04:55:43.6236339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6238205Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6239865Z         
2025-03-14T04:55:43.6240243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6240732Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:55:43.6241002Z         
2025-03-14T04:55:43.6241341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6242077Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6242627Z         
2025-03-14T04:55:43.6242978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6244844Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6246509Z         
2025-03-14T04:55:43.6246884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6247373Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:55:43.6247641Z         
2025-03-14T04:55:43.6247983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6248731Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6249280Z         
2025-03-14T04:55:43.6249633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6251508Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6253170Z         
2025-03-14T04:55:43.6253510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6254260Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.6254827Z         
2025-03-14T04:55:43.6255179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6257047Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6258768Z         
2025-03-14T04:55:43.6259156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6259665Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:55:43.6259952Z         
2025-03-14T04:55:43.6260345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6260866Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:55:43.6261148Z         
2025-03-14T04:55:43.6261483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6262246Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6262801Z         
2025-03-14T04:55:43.6263161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6265281Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6267103Z         
2025-03-14T04:55:43.6267502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6268022Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:55:43.6268307Z         
2025-03-14T04:55:43.6268663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6269456Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6270042Z         
2025-03-14T04:55:43.6270414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6272362Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6274003Z         
2025-03-14T04:55:43.6274371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6274859Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:55:43.6275124Z         
2025-03-14T04:55:43.6275468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6276217Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6276777Z         
2025-03-14T04:55:43.6277122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6279005Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6280663Z         
2025-03-14T04:55:43.6281030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6281651Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:55:43.6281938Z         
2025-03-14T04:55:43.6282319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6282815Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:55:43.6283089Z         
2025-03-14T04:55:43.6283427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6284158Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6284697Z         
2025-03-14T04:55:43.6285047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6286892Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6288504Z         
2025-03-14T04:55:43.6288877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6289365Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:55:43.6289631Z         
2025-03-14T04:55:43.6289970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6290706Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6291250Z         
2025-03-14T04:55:43.6291601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6293523Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6295195Z         
2025-03-14T04:55:43.6295569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6296054Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:55:43.6296322Z         
2025-03-14T04:55:43.6296665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6297406Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6297961Z         
2025-03-14T04:55:43.6298311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6300144Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6301762Z         
2025-03-14T04:55:43.6302127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6302618Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:55:43.6302898Z         
2025-03-14T04:55:43.6303267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6303753Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:55:43.6304028Z         
2025-03-14T04:55:43.6304417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6305153Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6305752Z         
2025-03-14T04:55:43.6306196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6308142Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6309824Z         
2025-03-14T04:55:43.6310197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6310682Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:55:43.6310946Z         
2025-03-14T04:55:43.6311283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6312017Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6312560Z         
2025-03-14T04:55:43.6312912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6314739Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6316370Z         
2025-03-14T04:55:43.6316742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6317220Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:55:43.6317488Z         
2025-03-14T04:55:43.6317824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6318556Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6319102Z         
2025-03-14T04:55:43.6319451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6321324Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6322978Z         
2025-03-14T04:55:43.6323339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6323823Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:55:43.6324095Z         
2025-03-14T04:55:43.6324461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6324954Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:55:43.6325217Z         
2025-03-14T04:55:43.6325555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6326274Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6326808Z         
2025-03-14T04:55:43.6327152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6328988Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6330589Z         
2025-03-14T04:55:43.6330960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6331429Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:55:43.6331687Z         
2025-03-14T04:55:43.6332020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6332746Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6333278Z         
2025-03-14T04:55:43.6333663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6335506Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6337132Z         
2025-03-14T04:55:43.6337504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6337979Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:55:43.6338234Z         
2025-03-14T04:55:43.6338568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6339289Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6339826Z         
2025-03-14T04:55:43.6340171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6341992Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6343593Z         
2025-03-14T04:55:43.6343940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6344783Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.6345375Z         
2025-03-14T04:55:43.6345725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6347746Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6349508Z         
2025-03-14T04:55:43.6349901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6350401Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:55:43.6350676Z         
2025-03-14T04:55:43.6351070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6351580Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:55:43.6351853Z         
2025-03-14T04:55:43.6352205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6352973Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6353531Z         
2025-03-14T04:55:43.6353904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6355749Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6357369Z         
2025-03-14T04:55:43.6357736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6358217Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:55:43.6358471Z         
2025-03-14T04:55:43.6358803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6359542Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6360085Z         
2025-03-14T04:55:43.6360436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6362297Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6363934Z         
2025-03-14T04:55:43.6364309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6364790Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:55:43.6365048Z         
2025-03-14T04:55:43.6365391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6366125Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6366673Z         
2025-03-14T04:55:43.6367026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6368852Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6370473Z         
2025-03-14T04:55:43.6370845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6371334Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:55:43.6371601Z         
2025-03-14T04:55:43.6371975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6372461Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:55:43.6372723Z         
2025-03-14T04:55:43.6373060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6373783Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6374316Z         
2025-03-14T04:55:43.6374665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6376509Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6378147Z         
2025-03-14T04:55:43.6378524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6379008Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:55:43.6379271Z         
2025-03-14T04:55:43.6379613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6380357Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6380910Z         
2025-03-14T04:55:43.6381266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6383325Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6385121Z         
2025-03-14T04:55:43.6385544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6386101Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:55:43.6386365Z         
2025-03-14T04:55:43.6386735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6387566Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6388150Z         
2025-03-14T04:55:43.6388509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6390543Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6392272Z         
2025-03-14T04:55:43.6392657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6393172Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:55:43.6393465Z         
2025-03-14T04:55:43.6393859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6394380Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:55:43.6394659Z         
2025-03-14T04:55:43.6395013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6395824Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6396385Z         
2025-03-14T04:55:43.6396752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6398714Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6400341Z         
2025-03-14T04:55:43.6400720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6401194Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:55:43.6401451Z         
2025-03-14T04:55:43.6401789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6402520Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6403084Z         
2025-03-14T04:55:43.6403449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6405426Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6407188Z         
2025-03-14T04:55:43.6407582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6408101Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:55:43.6408378Z         
2025-03-14T04:55:43.6408719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6409450Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6409988Z         
2025-03-14T04:55:43.6410339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6412206Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6413916Z         
2025-03-14T04:55:43.6414309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6414819Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:55:43.6415098Z         
2025-03-14T04:55:43.6415488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6415985Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:55:43.6416264Z         
2025-03-14T04:55:43.6416620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6417376Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6417934Z         
2025-03-14T04:55:43.6418346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6420304Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6422051Z         
2025-03-14T04:55:43.6422446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6422946Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:55:43.6423216Z         
2025-03-14T04:55:43.6423568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6424426Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6425028Z         
2025-03-14T04:55:43.6425427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6427395Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6429026Z         
2025-03-14T04:55:43.6429399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6429877Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:55:43.6430132Z         
2025-03-14T04:55:43.6430468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6431198Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6431735Z         
2025-03-14T04:55:43.6432084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6433940Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6435577Z         
2025-03-14T04:55:43.6435947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6436431Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:55:43.6436694Z         
2025-03-14T04:55:43.6437059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6437545Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:55:43.6437807Z         
2025-03-14T04:55:43.6438146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6438867Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6439397Z         
2025-03-14T04:55:43.6439749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6441594Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6443215Z         
2025-03-14T04:55:43.6443590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6444068Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:55:43.6444331Z         
2025-03-14T04:55:43.6444668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6445401Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6445940Z         
2025-03-14T04:55:43.6446328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6448158Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6449792Z         
2025-03-14T04:55:43.6450166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6450640Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:55:43.6450901Z         
2025-03-14T04:55:43.6451235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6451959Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6452496Z         
2025-03-14T04:55:43.6452844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6454668Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6456270Z         
2025-03-14T04:55:43.6456636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6457113Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:55:43.6457378Z         
2025-03-14T04:55:43.6457746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6458226Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:55:43.6458486Z         
2025-03-14T04:55:43.6458819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6459566Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6460100Z         
2025-03-14T04:55:43.6460453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6462285Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6464029Z         
2025-03-14T04:55:43.6464487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6465003Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:55:43.6465286Z         
2025-03-14T04:55:43.6465623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6466389Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6466964Z         
2025-03-14T04:55:43.6467337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6469323Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6471055Z         
2025-03-14T04:55:43.6471448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6471952Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:55:43.6472225Z         
2025-03-14T04:55:43.6472580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6473360Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6473921Z         
2025-03-14T04:55:43.6474311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6476152Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6477789Z         
2025-03-14T04:55:43.6478169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6478648Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:55:43.6478918Z         
2025-03-14T04:55:43.6479291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6479771Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:55:43.6480033Z         
2025-03-14T04:55:43.6480371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6481102Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6481854Z         
2025-03-14T04:55:43.6482211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6484042Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6485657Z         
2025-03-14T04:55:43.6486019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6486507Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:55:43.6486759Z         
2025-03-14T04:55:43.6487099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6487899Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6488449Z         
2025-03-14T04:55:43.6488798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6490663Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6492282Z         
2025-03-14T04:55:43.6492683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6493162Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:55:43.6493419Z         
2025-03-14T04:55:43.6493752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6494479Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6495005Z         
2025-03-14T04:55:43.6495346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6497092Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6498668Z         
2025-03-14T04:55:43.6499031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6499507Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:55:43.6499773Z         
2025-03-14T04:55:43.6500141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6500623Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:55:43.6500885Z         
2025-03-14T04:55:43.6501221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6501984Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6502521Z         
2025-03-14T04:55:43.6502901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6504800Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6506485Z         
2025-03-14T04:55:43.6506880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6507404Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:55:43.6507682Z         
2025-03-14T04:55:43.6508038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6508773Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6509332Z         
2025-03-14T04:55:43.6509704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6511663Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6511740Z         
2025-03-14T04:55:43.6512039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6512199Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:55:43.6512266Z         
2025-03-14T04:55:43.6512539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6513034Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6513112Z         
2025-03-14T04:55:43.6513391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6515026Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6515104Z         
2025-03-14T04:55:43.6515398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6515567Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:55:43.6515639Z         
2025-03-14T04:55:43.6515941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6516089Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:55:43.6516165Z         
2025-03-14T04:55:43.6516430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6516874Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6516942Z         
2025-03-14T04:55:43.6517215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6518729Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6518796Z         
2025-03-14T04:55:43.6519087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6519225Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:55:43.6519299Z         
2025-03-14T04:55:43.6519545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6520003Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6520070Z         
2025-03-14T04:55:43.6520414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6521942Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6522011Z         
2025-03-14T04:55:43.6522302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6522439Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:55:43.6522509Z         
2025-03-14T04:55:43.6522756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6523184Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6523249Z         
2025-03-14T04:55:43.6523523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6525028Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6525096Z         
2025-03-14T04:55:43.6525388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6525540Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:55:43.6525614Z         
2025-03-14T04:55:43.6525894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6526044Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:55:43.6526108Z         
2025-03-14T04:55:43.6526395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6526817Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6526921Z         
2025-03-14T04:55:43.6527182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6528718Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6528798Z         
2025-03-14T04:55:43.6529081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6529226Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:55:43.6529293Z         
2025-03-14T04:55:43.6529549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6529967Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6530045Z         
2025-03-14T04:55:43.6530305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6531823Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6531901Z         
2025-03-14T04:55:43.6532183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6532329Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:55:43.6532394Z         
2025-03-14T04:55:43.6532651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6533104Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6533208Z         
2025-03-14T04:55:43.6533478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6535015Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6535092Z         
2025-03-14T04:55:43.6535372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6535527Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:55:43.6535593Z         
2025-03-14T04:55:43.6535885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6536028Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:55:43.6536103Z         
2025-03-14T04:55:43.6536352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6536772Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6536847Z         
2025-03-14T04:55:43.6537113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6538635Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6538703Z         
2025-03-14T04:55:43.6538993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6539135Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:55:43.6539240Z         
2025-03-14T04:55:43.6539499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6539928Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6540029Z         
2025-03-14T04:55:43.6540292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6541804Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6541883Z         
2025-03-14T04:55:43.6542164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6542306Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:55:43.6542371Z         
2025-03-14T04:55:43.6542629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6543051Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6543126Z         
2025-03-14T04:55:43.6543387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6545047Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6545130Z         
2025-03-14T04:55:43.6545427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6545589Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:55:43.6545659Z         
2025-03-14T04:55:43.6545995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6546139Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:55:43.6546213Z         
2025-03-14T04:55:43.6546474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6546946Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6547016Z         
2025-03-14T04:55:43.6547301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6548917Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6548998Z         
2025-03-14T04:55:43.6549303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6549447Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:55:43.6549521Z         
2025-03-14T04:55:43.6549785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6550231Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6550300Z         
2025-03-14T04:55:43.6550580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6552179Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6552261Z         
2025-03-14T04:55:43.6552564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6552741Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:55:43.6552819Z         
2025-03-14T04:55:43.6553078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6553527Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6553631Z         
2025-03-14T04:55:43.6553916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6555526Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6555597Z         
2025-03-14T04:55:43.6555900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6556058Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:55:43.6556137Z         
2025-03-14T04:55:43.6556436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6556594Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:55:43.6556664Z         
2025-03-14T04:55:43.6556933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6557373Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6557451Z         
2025-03-14T04:55:43.6557731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6559355Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6559433Z         
2025-03-14T04:55:43.6559749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6559895Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:55:43.6559962Z         
2025-03-14T04:55:43.6560256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6560675Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6560749Z         
2025-03-14T04:55:43.6561011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6562534Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6562612Z         
2025-03-14T04:55:43.6562902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6563048Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:55:43.6563112Z         
2025-03-14T04:55:43.6563369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6563795Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6563872Z         
2025-03-14T04:55:43.6564134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6565667Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6565744Z         
2025-03-14T04:55:43.6566021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6566204Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:55:43.6566270Z         
2025-03-14T04:55:43.6566564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6567354Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:55:43.6567430Z         
2025-03-14T04:55:43.6567685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6568115Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6568180Z         
2025-03-14T04:55:43.6568456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6570011Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6570081Z         
2025-03-14T04:55:43.6570387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6570518Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:55:43.6570591Z         
2025-03-14T04:55:43.6570846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6571269Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6571345Z         
2025-03-14T04:55:43.6571615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6573138Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6573205Z         
2025-03-14T04:55:43.6573535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6573670Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:55:43.6573769Z         
2025-03-14T04:55:43.6574017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6574442Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6574515Z         
2025-03-14T04:55:43.6574778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6576299Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6576367Z         
2025-03-14T04:55:43.6576657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6576804Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:55:43.6576877Z         
2025-03-14T04:55:43.6577159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6577305Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:55:43.6577377Z         
2025-03-14T04:55:43.6577626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6578048Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6578111Z         
2025-03-14T04:55:43.6578379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6579924Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6579997Z         
2025-03-14T04:55:43.6580288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6580452Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:55:43.6580522Z         
2025-03-14T04:55:43.6580769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6581195Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6581259Z         
2025-03-14T04:55:43.6581647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6583223Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6583305Z         
2025-03-14T04:55:43.6583615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6583764Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:55:43.6583838Z         
2025-03-14T04:55:43.6584123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6584577Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6584647Z         
2025-03-14T04:55:43.6584936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6586635Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6586721Z         
2025-03-14T04:55:43.6587023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6587179Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:55:43.6587317Z         
2025-03-14T04:55:43.6587616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6587774Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:55:43.6587845Z         
2025-03-14T04:55:43.6588121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6588564Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6588646Z         
2025-03-14T04:55:43.6588930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6590551Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6590634Z         
2025-03-14T04:55:43.6590934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6591087Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:55:43.6591160Z         
2025-03-14T04:55:43.6591434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6591876Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6591957Z         
2025-03-14T04:55:43.6592236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6593870Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6593949Z         
2025-03-14T04:55:43.6594278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6594427Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:55:43.6594495Z         
2025-03-14T04:55:43.6594777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6595227Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6595304Z         
2025-03-14T04:55:43.6595579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6597216Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6597289Z         
2025-03-14T04:55:43.6597565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6597726Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:55:43.6597795Z         
2025-03-14T04:55:43.6598097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6598242Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:55:43.6598320Z         
2025-03-14T04:55:43.6598585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6599285Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6599375Z         
2025-03-14T04:55:43.6599661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6601446Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6601544Z         
2025-03-14T04:55:43.6601852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6602026Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:55:43.6602129Z         
2025-03-14T04:55:43.6602405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6602947Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6603021Z         
2025-03-14T04:55:43.6603306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6605007Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6605078Z         
2025-03-14T04:55:43.6605385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6605537Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:55:43.6605612Z         
2025-03-14T04:55:43.6605861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6606300Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6606366Z         
2025-03-14T04:55:43.6606634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6608382Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6608485Z         
2025-03-14T04:55:43.6608799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6609032Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:55:43.6609109Z         
2025-03-14T04:55:43.6609409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6609564Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:55:43.6609633Z         
2025-03-14T04:55:43.6609903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6610356Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6610428Z         
2025-03-14T04:55:43.6610714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6612324Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6612402Z         
2025-03-14T04:55:43.6612702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6612859Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:55:43.6612927Z         
2025-03-14T04:55:43.6613202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6613659Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6613732Z         
2025-03-14T04:55:43.6614019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6615658Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6615768Z         
2025-03-14T04:55:43.6616170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6616328Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:55:43.6616406Z         
2025-03-14T04:55:43.6616734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6617282Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6617356Z         
2025-03-14T04:55:43.6617639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6619295Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6619385Z         
2025-03-14T04:55:43.6619703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6619875Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:55:43.6619954Z         
2025-03-14T04:55:43.6620269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6620442Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:55:43.6620514Z         
2025-03-14T04:55:43.6620796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6621269Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6621351Z         
2025-03-14T04:55:43.6621643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6623696Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6623849Z         
2025-03-14T04:55:43.6624286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6624464Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:55:43.6624559Z         
2025-03-14T04:55:43.6624928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6625435Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6625555Z         
2025-03-14T04:55:43.6625854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6627681Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6627763Z         
2025-03-14T04:55:43.6628065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6628222Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:55:43.6628296Z         
2025-03-14T04:55:43.6628582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6629056Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6629134Z         
2025-03-14T04:55:43.6629409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6631225Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6631340Z         
2025-03-14T04:55:43.6631753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6631947Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:55:43.6632023Z         
2025-03-14T04:55:43.6632403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6632604Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:55:43.6632697Z         
2025-03-14T04:55:43.6632984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6633577Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6633659Z         
2025-03-14T04:55:43.6633954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6635741Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6635846Z         
2025-03-14T04:55:43.6636199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6636358Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:55:43.6636450Z         
2025-03-14T04:55:43.6636833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6637334Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6637413Z         
2025-03-14T04:55:43.6637698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6639393Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6639495Z         
2025-03-14T04:55:43.6639816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6639975Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:55:43.6640049Z         
2025-03-14T04:55:43.6640335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6640812Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6640891Z         
2025-03-14T04:55:43.6641196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6642847Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6642928Z         
2025-03-14T04:55:43.6643232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6643413Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:55:43.6643484Z         
2025-03-14T04:55:43.6643801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6643961Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:55:43.6644037Z         
2025-03-14T04:55:43.6644312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6644774Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6644844Z         
2025-03-14T04:55:43.6645177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6646784Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6646893Z         
2025-03-14T04:55:43.6647197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6647340Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:55:43.6647417Z         
2025-03-14T04:55:43.6647678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6648130Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6648199Z         
2025-03-14T04:55:43.6648484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6650072Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6650150Z         
2025-03-14T04:55:43.6650455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6650596Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:55:43.6650674Z         
2025-03-14T04:55:43.6650935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6651389Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6651457Z         
2025-03-14T04:55:43.6651739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6653362Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6653470Z         
2025-03-14T04:55:43.6653776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6653942Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:55:43.6654019Z         
2025-03-14T04:55:43.6654320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6654483Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:55:43.6654552Z         
2025-03-14T04:55:43.6654826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6655270Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6655347Z         
2025-03-14T04:55:43.6655626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6657256Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6657332Z         
2025-03-14T04:55:43.6657633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6657787Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:55:43.6657856Z         
2025-03-14T04:55:43.6658126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6658576Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6658707Z         
2025-03-14T04:55:43.6658996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6660695Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6660811Z         
2025-03-14T04:55:43.6661127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6661287Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:55:43.6661360Z         
2025-03-14T04:55:43.6661642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6662116Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6662195Z         
2025-03-14T04:55:43.6662489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6664269Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6664362Z         
2025-03-14T04:55:43.6664671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6664855Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:55:43.6664932Z         
2025-03-14T04:55:43.6665264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6665427Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:55:43.6665512Z         
2025-03-14T04:55:43.6665797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6666326Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6666402Z         
2025-03-14T04:55:43.6666707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6668468Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6668543Z         
2025-03-14T04:55:43.6668868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6669020Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:55:43.6669102Z         
2025-03-14T04:55:43.6669379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6669866Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6669938Z         
2025-03-14T04:55:43.6670239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6671958Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6672032Z         
2025-03-14T04:55:43.6672358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6672510Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:55:43.6672591Z         
2025-03-14T04:55:43.6672880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6673375Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6673452Z         
2025-03-14T04:55:43.6673726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6675329Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6675429Z         
2025-03-14T04:55:43.6675701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6676160Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.6676236Z         
2025-03-14T04:55:43.6676515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6678154Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6678235Z         
2025-03-14T04:55:43.6678524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6678691Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:55:43.6678759Z         
2025-03-14T04:55:43.6679061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6679227Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:55:43.6679294Z         
2025-03-14T04:55:43.6679563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6679992Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6680066Z         
2025-03-14T04:55:43.6680373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6682114Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6682276Z         
2025-03-14T04:55:43.6682584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6682740Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:55:43.6682817Z         
2025-03-14T04:55:43.6683112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6683582Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6683661Z         
2025-03-14T04:55:43.6683948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6685548Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6685629Z         
2025-03-14T04:55:43.6685938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6686089Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:55:43.6686162Z         
2025-03-14T04:55:43.6686439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6686885Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6686962Z         
2025-03-14T04:55:43.6687242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6688896Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6689004Z         
2025-03-14T04:55:43.6689305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6689485Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:55:43.6689554Z         
2025-03-14T04:55:43.6689861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6690021Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:55:43.6690098Z         
2025-03-14T04:55:43.6690360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6690810Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.6690880Z         
2025-03-14T04:55:43.6691166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6692803Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6692880Z         
2025-03-14T04:55:43.6693186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6693334Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:55:43.6693425Z         
2025-03-14T04:55:43.6693688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6694145Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.6694246Z         
2025-03-14T04:55:43.6694533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6696095Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6696214Z         
2025-03-14T04:55:43.6696523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6696668Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:55:43.6696743Z         
2025-03-14T04:55:43.6697014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6697442Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.6697506Z         
2025-03-14T04:55:43.6697779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.6699291Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.6699360Z         
2025-03-14T04:55:43.6699645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.6699804Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:55:43.6699881Z         
2025-03-14T04:55:43.6700159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.6700314Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:55:43.6700379Z         
2025-03-14T04:55:43.6700820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.6701007Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.6701088Z         
2025-03-14T04:55:43.6701387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.6701565Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:43.6701632Z         
2025-03-14T04:55:43.6702071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.6702225Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.6702298Z         
2025-03-14T04:55:43.6702593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.6702748Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:55:43.6702817Z         
2025-03-14T04:55:43.6703217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:43.6703410Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:55:43.6703522Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:55:43.6703650Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:55:43.6703727Z         
2025-03-14T04:55:43.6704071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:43.6704285Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:55:43.6704362Z         
2025-03-14T04:55:43.6704736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:43.6704883Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:55:43.6704957Z         
2025-03-14T04:55:43.6705403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:43.6705658Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:55:43.6705740Z         
2025-03-14T04:55:43.6706205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:43.6706362Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:55:43.6706819Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:55:43.6706955Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:55:43.6707074Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:55:43.6707147Z         
2025-03-14T04:55:43.6707446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.6707580Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:55:43.6707679Z         
2025-03-14T04:55:43.6707944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.6708720Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:55:43.6708895Z         
2025-03-14T04:55:43.6709175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:43.6709378Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:55:43.6709443Z         
2025-03-14T04:55:43.6709833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:43.6710706Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:55:43.6710772Z         
2025-03-14T04:55:43.6711142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:43.6711950Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:55:43.6712026Z         
2025-03-14T04:55:43.6712365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:55:43.6712525Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:55:43.6712680Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:55:43.6712746Z         
2025-03-14T04:55:43.6713167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:55:43.6713330Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:55:43.6713510Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:55:43.6713687Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:55:43.6713761Z         
2025-03-14T04:55:43.6714187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:43.6714400Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:55:43.6714468Z         
2025-03-14T04:55:43.6714904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:43.6715082Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:55:43.6715237Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:43.6715376Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:43.6715450Z         
2025-03-14T04:55:43.6715823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:43.6716004Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:43.6716069Z         
2025-03-14T04:55:43.6716389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:43.6716531Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:43.6716608Z         
2025-03-14T04:55:43.6716936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:43.6717080Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:43.6717213Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.6717376Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:55:43.6717445Z         
2025-03-14T04:55:43.6717785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:43.6717917Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:43.6718051Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:43.6718205Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:55:43.6718284Z         
2025-03-14T04:55:43.6718608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:43.6718746Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.6718842Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:55:43.6718983Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:55:43.6719052Z         
2025-03-14T04:55:43.6719386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:43.6719540Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:43.6719653Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:55:43.6719782Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:55:43.6719856Z         
2025-03-14T04:55:43.6720220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:43.6720426Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:43.6720554Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:55:43.6720621Z         
2025-03-14T04:55:43.6720933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:43.6721124Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:43.6721247Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:55:43.6721315Z         
2025-03-14T04:55:43.6721631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:43.6724582Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:43.6724719Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:55:43.6724787Z         
2025-03-14T04:55:43.6725118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:43.6725312Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:43.6725435Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:55:43.6725504Z         
2025-03-14T04:55:43.6725858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:43.6726002Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:43.6726109Z         
2025-03-14T04:55:43.6726465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:43.6726605Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:43.6726681Z         
2025-03-14T04:55:43.6727036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:43.6727190Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:43.6727318Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:55:43.6727484Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:43.6727628Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:55:43.6727704Z         
2025-03-14T04:55:43.6728065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:43.6728214Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:43.6728340Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:55:43.6728501Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:43.6728648Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:55:43.6728717Z         
2025-03-14T04:55:43.6729063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:43.6729186Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:43.6729405Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:43.6729546Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:55:43.6729620Z         
2025-03-14T04:55:43.6729980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:43.6730110Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:43.6730279Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:43.6730420Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:55:43.6730547Z         
2025-03-14T04:55:43.6730883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:43.6730987Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:43.6731120Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:43.6731191Z         
2025-03-14T04:55:43.6731522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:43.6731623Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:43.6731749Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:43.6731819Z         
2025-03-14T04:55:43.6732144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:43.6732267Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:43.6732411Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:43.6732481Z         
2025-03-14T04:55:43.6732803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:43.6732922Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:43.6733058Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:43.6733128Z         
2025-03-14T04:55:43.6733494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:43.6733684Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:43.6733761Z         
2025-03-14T04:55:43.6734107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:43.6734284Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:55:43.6734355Z         
2025-03-14T04:55:43.6734759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:43.6734942Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:43.6735022Z         
2025-03-14T04:55:43.6735519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:43.6735703Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:43.6735773Z         
2025-03-14T04:55:43.6736092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.6736249Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:55:43.6736326Z         
2025-03-14T04:55:43.6736757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:43.6736882Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:55:43.6736988Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:55:43.6737173Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:43.6737241Z         
2025-03-14T04:55:43.6737716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:43.6737894Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:43.6738134Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:55:43.6738213Z         
2025-03-14T04:55:43.6738673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:43.6738853Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:43.6738924Z         
2025-03-14T04:55:43.6739233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.6739390Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:55:43.6739464Z         
2025-03-14T04:55:43.6739852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:43.6740009Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:55:43.6740076Z         
2025-03-14T04:55:43.6740387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.6740535Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:55:43.6740609Z         
2025-03-14T04:55:43.6740991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:43.6741141Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:55:43.6741207Z         
2025-03-14T04:55:43.6741702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:43.6741846Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:55:43.6741976Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:43.6742165Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:43.6742309Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:43.6742376Z         
2025-03-14T04:55:43.6742761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:43.6742898Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:43.6742974Z         
2025-03-14T04:55:43.6743531Z 
2025-03-14T04:55:43.6743630Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:43.6845990Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:55:43.6846857Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:55:43.6847216Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6847590Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6847968Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6848367Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6848734Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6849100Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6849490Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6849937Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6850335Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6850751Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6851098Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6851548Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6852032Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6852413Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6852832Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6853148Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6853549Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6853985Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6854375Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6854756Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6855135Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.6855571Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6855999Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6856419Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.6856812Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.6857139Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6857550Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6857976Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6858371Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6858768Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6859128Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6859583Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6860007Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6860397Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6860801Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6861156Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6861595Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6862007Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6862384Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6862733Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6863053Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6863441Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6863816Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6864311Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6864726Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6865108Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6865529Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6865917Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6866290Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6866647Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6866984Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6867410Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6867794Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6868170Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6868528Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6868856Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6869260Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6869643Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6870011Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6870378Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6870709Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6871100Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6871472Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6871839Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6872197Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6872524Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6872918Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6873289Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6873652Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6873998Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6874383Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.6874779Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6875175Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6875566Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.6875934Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.6876277Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6876653Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6877041Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6877396Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6877748Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6878070Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6878453Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6878827Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6879190Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6879544Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6879863Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6880250Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6880627Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6880992Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6881343Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6881908Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6882284Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6882687Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6883039Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6883393Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6883697Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6884052Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6884406Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6884736Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6885074Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6885371Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6885734Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6886088Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6886417Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6886775Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6887076Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6887426Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6887782Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6888114Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6888483Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6888773Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6889140Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6889483Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6889815Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6890164Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6890459Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6890822Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6891173Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6891515Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6891843Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6892146Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6892501Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6892858Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6893197Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6893525Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6893828Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6894181Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6894548Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6894869Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6895229Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6895515Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6895875Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6896213Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6896552Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6896867Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6897165Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.6897524Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6897872Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6898215Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.6898541Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.6898832Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6899178Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6899513Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6899839Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6900147Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6900436Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6900771Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6901112Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6901472Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6901790Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6902096Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6902436Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6902792Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6903153Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6903486Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6903786Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6904243Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6904620Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6904970Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6905320Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6905640Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6906050Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6906443Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6906826Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6907163Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6907477Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6907842Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6908249Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6908595Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6908954Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6909278Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6909650Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6910025Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6910363Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6910701Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6911002Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6911365Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6911730Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6912063Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6912408Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6912707Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6913079Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6913444Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6913787Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6914116Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6914423Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6914796Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6915190Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6915534Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6915875Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6916179Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6916550Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6916931Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6917275Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6917616Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6917927Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6918304Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6918667Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6919007Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6919350Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6919655Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6920032Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6920392Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6920744Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6921081Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6921382Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6921788Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6922144Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6922512Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6922836Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6923149Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6923503Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6923848Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6924175Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6924486Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6924774Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6925115Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6925455Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6925777Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6926098Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6926385Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6926736Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6927077Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6927400Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6927720Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6928002Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6928381Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6928720Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6929064Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6929378Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6929668Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6930035Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6930372Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6930701Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6931014Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6931312Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6931656Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6932001Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6932320Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6932640Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6932938Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6933280Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6933630Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6933952Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6934275Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6934606Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6934954Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6935310Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6935642Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6935965Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6936268Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6936612Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6936947Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6937275Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6937587Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6937882Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6938218Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6938556Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6938883Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6939194Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6939486Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6939824Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6940163Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6940478Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6940791Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6941108Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6941456Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6941818Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6942141Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6942479Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6942765Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6943117Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6943457Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6943787Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6944160Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6944502Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6944897Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6945289Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6945662Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6945999Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6946316Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6946700Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6947096Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6947453Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6947854Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6948189Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6948586Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6948982Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6949353Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6949727Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6950050Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6950444Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6950815Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6951193Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6951553Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6951870Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6952265Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6952645Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6953012Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6953361Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6953687Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6954070Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6954461Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6954867Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6955222Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6955565Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6955953Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6956299Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6956659Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6956976Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6957264Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6957608Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6957949Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6958275Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6958593Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6958877Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6959221Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6959551Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6959879Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6960186Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6960480Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6960827Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6961216Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6961543Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6961855Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6962164Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6962504Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6962866Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6963188Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6963509Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6963801Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6964141Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6964489Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6964810Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6965129Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6965417Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6965766Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6966104Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6966433Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6966752Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6967037Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6967380Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6967749Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6968080Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6968412Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6968703Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6969042Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6969403Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6969727Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6970036Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6970328Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6970675Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6971013Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6971332Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6971651Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6971933Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6972281Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6972619Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6972938Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6973252Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6973536Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6973914Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6974252Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6974594Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6974906Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6975198Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6975561Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6975896Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6976225Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6976538Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6976833Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6977178Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6977533Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6977861Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6978189Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6978483Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6978828Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6979177Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6979508Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6979829Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6980141Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6980494Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6980859Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6981188Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6981614Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6981956Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6982313Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6982655Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6982985Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6983296Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6983595Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6983983Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6984431Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6984827Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6985243Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6985539Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6985907Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6986318Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6986666Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6987051Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6987365Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6987733Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6988122Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6988476Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6988834Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6989134Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6989511Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6989871Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6990228Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6990571Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6990874Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6991242Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6991606Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6991962Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6992295Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6992604Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6992963Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6993326Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6993655Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6993995Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6994290Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6994644Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6994989Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6995309Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.6995648Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.6995939Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:55:43.6996293Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6996642Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6996971Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.6997291Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.6997587Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:55:43.6997981Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6998339Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.6998701Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.6999012Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.6999308Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:55:43.6999658Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.6999998Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7000357Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7000670Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7000982Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7001320Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7001673Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7002009Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7002326Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7002619Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7002960Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7003305Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7003629Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7003945Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7004232Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7004579Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7004917Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7005245Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7005559Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7005847Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7006193Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7006576Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7006905Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7007235Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7007534Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7007873Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7008233Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7008560Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7008873Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7009167Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7009503Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7009850Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7010168Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7010489Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7010775Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7011120Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7011465Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7011782Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7012102Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7012398Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7012755Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7013118Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7013447Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7013775Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7014066Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7014412Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7014765Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7015094Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7015404Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7015714Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.7016074Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7016428Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7016763Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.7017093Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.7017382Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7017724Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7018066Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7018387Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7018706Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7018989Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7019361Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7019699Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7020038Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7020357Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7020644Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7021007Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7021344Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7021670Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7021977Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7022271Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7022609Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7022951Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7023277Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7023587Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7023876Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7024270Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7024630Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7024970Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7025320Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7025677Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7026057Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7026429Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7026763Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7027083Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7027451Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:55:43.7027807Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:55:43.7028166Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:55:43.7028599Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:55:43.7029006Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:55:43.7029411Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:55:43.7029808Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:55:43.7029892Z         
2025-03-14T04:55:43.7030237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7030774Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7030859Z         
2025-03-14T04:55:43.7031192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7032840Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7032935Z         
2025-03-14T04:55:43.7033266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:55:43.7033421Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:55:43.7033491Z         
2025-03-14T04:55:43.7033865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:55:43.7034119Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:55:43.7034195Z         
2025-03-14T04:55:43.7034453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7034895Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7034964Z         
2025-03-14T04:55:43.7035241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7036772Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7036840Z         
2025-03-14T04:55:43.7037136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7037279Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:55:43.7037351Z         
2025-03-14T04:55:43.7037604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7038035Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7038102Z         
2025-03-14T04:55:43.7038375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7039926Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7039997Z         
2025-03-14T04:55:43.7040290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7040447Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:55:43.7040521Z         
2025-03-14T04:55:43.7040770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7041207Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7041288Z         
2025-03-14T04:55:43.7041566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7043080Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7043150Z         
2025-03-14T04:55:43.7043409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7043844Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7043921Z         
2025-03-14T04:55:43.7044184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7045744Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7045820Z         
2025-03-14T04:55:43.7046099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7046255Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:55:43.7046349Z         
2025-03-14T04:55:43.7046643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7046798Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:55:43.7046886Z         
2025-03-14T04:55:43.7047139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7047572Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7047653Z         
2025-03-14T04:55:43.7047926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7049441Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7049511Z         
2025-03-14T04:55:43.7049809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7049952Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:55:43.7050026Z         
2025-03-14T04:55:43.7050275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7050706Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7050771Z         
2025-03-14T04:55:43.7051046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7052568Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7052637Z         
2025-03-14T04:55:43.7052959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7053102Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:55:43.7053176Z         
2025-03-14T04:55:43.7053426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7053881Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7053947Z         
2025-03-14T04:55:43.7054219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7055758Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7055827Z         
2025-03-14T04:55:43.7056114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7056272Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:55:43.7056349Z         
2025-03-14T04:55:43.7056630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7056789Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:55:43.7056857Z         
2025-03-14T04:55:43.7057114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7057536Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7057610Z         
2025-03-14T04:55:43.7057881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7059473Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7059557Z         
2025-03-14T04:55:43.7059857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7060014Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:55:43.7060098Z         
2025-03-14T04:55:43.7060372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7060845Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7060944Z         
2025-03-14T04:55:43.7061237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7062854Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7062933Z         
2025-03-14T04:55:43.7063239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7063396Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:55:43.7063465Z         
2025-03-14T04:55:43.7063742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7064263Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7064339Z         
2025-03-14T04:55:43.7064629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7066288Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7066374Z         
2025-03-14T04:55:43.7066747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7066923Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:55:43.7066998Z         
2025-03-14T04:55:43.7067302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7067489Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:55:43.7067558Z         
2025-03-14T04:55:43.7067827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7068284Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7068378Z         
2025-03-14T04:55:43.7068659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7070275Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7070357Z         
2025-03-14T04:55:43.7070656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7070819Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:55:43.7070890Z         
2025-03-14T04:55:43.7071162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7071617Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7071698Z         
2025-03-14T04:55:43.7071980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7073597Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7073676Z         
2025-03-14T04:55:43.7073966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7074133Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:55:43.7074200Z         
2025-03-14T04:55:43.7074463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7074894Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7074982Z         
2025-03-14T04:55:43.7075247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7076757Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7076837Z         
2025-03-14T04:55:43.7077085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7077530Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7077600Z         
2025-03-14T04:55:43.7077865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7079407Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7079483Z         
2025-03-14T04:55:43.7079767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7079917Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:55:43.7079989Z         
2025-03-14T04:55:43.7080300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7080465Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:55:43.7080555Z         
2025-03-14T04:55:43.7080815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7081232Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7081308Z         
2025-03-14T04:55:43.7081716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7083261Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7083337Z         
2025-03-14T04:55:43.7083628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7083784Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:55:43.7083853Z         
2025-03-14T04:55:43.7084111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7084539Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7084615Z         
2025-03-14T04:55:43.7084880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7086429Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7086505Z         
2025-03-14T04:55:43.7086790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7087011Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:55:43.7087079Z         
2025-03-14T04:55:43.7087338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7087794Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7087868Z         
2025-03-14T04:55:43.7088130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7089702Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7089780Z         
2025-03-14T04:55:43.7090074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7090244Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:55:43.7090310Z         
2025-03-14T04:55:43.7090602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7090756Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:55:43.7090828Z         
2025-03-14T04:55:43.7091080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7091510Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7091578Z         
2025-03-14T04:55:43.7091856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7093401Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7093500Z         
2025-03-14T04:55:43.7093799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7093949Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:55:43.7094043Z         
2025-03-14T04:55:43.7094302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7094748Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7094830Z         
2025-03-14T04:55:43.7095108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7096655Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7096726Z         
2025-03-14T04:55:43.7097026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7097170Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:55:43.7097246Z         
2025-03-14T04:55:43.7097503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7097947Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7098015Z         
2025-03-14T04:55:43.7098290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7099847Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7099917Z         
2025-03-14T04:55:43.7100289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7100453Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:55:43.7100529Z         
2025-03-14T04:55:43.7100819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7101000Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:55:43.7101067Z         
2025-03-14T04:55:43.7101336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7101778Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7101867Z         
2025-03-14T04:55:43.7102147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7103717Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7103800Z         
2025-03-14T04:55:43.7104151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7104326Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:55:43.7104394Z         
2025-03-14T04:55:43.7104658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7105104Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7105173Z         
2025-03-14T04:55:43.7105466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7107188Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7107275Z         
2025-03-14T04:55:43.7107599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7107767Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:55:43.7107864Z         
2025-03-14T04:55:43.7108143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7108628Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7108717Z         
2025-03-14T04:55:43.7109029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7110748Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7110833Z         
2025-03-14T04:55:43.7111161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7111336Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:55:43.7111420Z         
2025-03-14T04:55:43.7113832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7114427Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:55:43.7114741Z         
2025-03-14T04:55:43.7115131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7115934Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7116479Z         
2025-03-14T04:55:43.7116848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7118767Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7120519Z         
2025-03-14T04:55:43.7120939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7121458Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:55:43.7121738Z         
2025-03-14T04:55:43.7122100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7122886Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7123486Z         
2025-03-14T04:55:43.7123860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7125817Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7127557Z         
2025-03-14T04:55:43.7127949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7128459Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:55:43.7128733Z         
2025-03-14T04:55:43.7129157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7129948Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7130528Z         
2025-03-14T04:55:43.7130900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7132894Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7134610Z         
2025-03-14T04:55:43.7134976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7135764Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7136378Z         
2025-03-14T04:55:43.7136753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7138746Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7140537Z         
2025-03-14T04:55:43.7140931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7141447Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:55:43.7141722Z         
2025-03-14T04:55:43.7142120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7142634Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:55:43.7142921Z         
2025-03-14T04:55:43.7143311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7144195Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7144857Z         
2025-03-14T04:55:43.7145265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7147240Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7148864Z         
2025-03-14T04:55:43.7149270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7149754Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:55:43.7150014Z         
2025-03-14T04:55:43.7150368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7151116Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7151673Z         
2025-03-14T04:55:43.7152029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7153904Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7155538Z         
2025-03-14T04:55:43.7155915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7156398Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:55:43.7156660Z         
2025-03-14T04:55:43.7157005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7157792Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7158345Z         
2025-03-14T04:55:43.7158697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7160576Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7162230Z         
2025-03-14T04:55:43.7162605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7163120Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:55:43.7163392Z         
2025-03-14T04:55:43.7163768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7164283Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:55:43.7164553Z         
2025-03-14T04:55:43.7164900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7165636Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7166200Z         
2025-03-14T04:55:43.7166556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7168417Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7170096Z         
2025-03-14T04:55:43.7170481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7170988Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:55:43.7171263Z         
2025-03-14T04:55:43.7171652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7172424Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7172998Z         
2025-03-14T04:55:43.7173364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7175248Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7176925Z         
2025-03-14T04:55:43.7177344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7177839Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:55:43.7178106Z         
2025-03-14T04:55:43.7178475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7179211Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7179763Z         
2025-03-14T04:55:43.7180118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7182165Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7183971Z         
2025-03-14T04:55:43.7184455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7185011Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:55:43.7185315Z         
2025-03-14T04:55:43.7185702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7186195Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:55:43.7186555Z         
2025-03-14T04:55:43.7186897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7187629Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7188165Z         
2025-03-14T04:55:43.7188523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7190402Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7192032Z         
2025-03-14T04:55:43.7192412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7192931Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:55:43.7193192Z         
2025-03-14T04:55:43.7193524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7194262Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7194835Z         
2025-03-14T04:55:43.7195192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7197034Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7198660Z         
2025-03-14T04:55:43.7199030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7199511Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:55:43.7199775Z         
2025-03-14T04:55:43.7200139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7200875Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7201419Z         
2025-03-14T04:55:43.7201773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7203618Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7205239Z         
2025-03-14T04:55:43.7205623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7206108Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:55:43.7206395Z         
2025-03-14T04:55:43.7206771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7207261Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:55:43.7207528Z         
2025-03-14T04:55:43.7207865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7208597Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7209164Z         
2025-03-14T04:55:43.7209517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7211355Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7212971Z         
2025-03-14T04:55:43.7213356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7213869Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:55:43.7214161Z         
2025-03-14T04:55:43.7214507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7215251Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7215795Z         
2025-03-14T04:55:43.7216148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7218041Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7219825Z         
2025-03-14T04:55:43.7220198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7220731Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:55:43.7221005Z         
2025-03-14T04:55:43.7221366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7222142Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7222728Z         
2025-03-14T04:55:43.7223103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7225221Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7226998Z         
2025-03-14T04:55:43.7227392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7227901Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:55:43.7228188Z         
2025-03-14T04:55:43.7228614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7229128Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:55:43.7229405Z         
2025-03-14T04:55:43.7229763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7230520Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7231063Z         
2025-03-14T04:55:43.7231413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7233272Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7234911Z         
2025-03-14T04:55:43.7235287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7235777Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:55:43.7236044Z         
2025-03-14T04:55:43.7236382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7237110Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7237708Z         
2025-03-14T04:55:43.7238059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7239879Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7241496Z         
2025-03-14T04:55:43.7241871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7242346Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:55:43.7242622Z         
2025-03-14T04:55:43.7242953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7243691Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7244232Z         
2025-03-14T04:55:43.7244586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7246436Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7248023Z         
2025-03-14T04:55:43.7248387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7248884Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:55:43.7249145Z         
2025-03-14T04:55:43.7249519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7250004Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:55:43.7250266Z         
2025-03-14T04:55:43.7250606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7251352Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7251886Z         
2025-03-14T04:55:43.7252244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7254072Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7255694Z         
2025-03-14T04:55:43.7256098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7256587Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:55:43.7256847Z         
2025-03-14T04:55:43.7257183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7257910Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7258446Z         
2025-03-14T04:55:43.7258798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7260633Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7262231Z         
2025-03-14T04:55:43.7262632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7263113Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:55:43.7263375Z         
2025-03-14T04:55:43.7263718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7264520Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7265099Z         
2025-03-14T04:55:43.7265474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7267381Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7269003Z         
2025-03-14T04:55:43.7269371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7269860Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:55:43.7270151Z         
2025-03-14T04:55:43.7270528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7271015Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:55:43.7271284Z         
2025-03-14T04:55:43.7271624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7272353Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7272885Z         
2025-03-14T04:55:43.7273235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7275094Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7276735Z         
2025-03-14T04:55:43.7277107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7277586Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:55:43.7277844Z         
2025-03-14T04:55:43.7278179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7278931Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7279464Z         
2025-03-14T04:55:43.7279811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7281749Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7283412Z         
2025-03-14T04:55:43.7283865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7284343Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:55:43.7284604Z         
2025-03-14T04:55:43.7284943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7285700Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7286279Z         
2025-03-14T04:55:43.7286656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7288633Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7290343Z         
2025-03-14T04:55:43.7290758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7291267Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:55:43.7291549Z         
2025-03-14T04:55:43.7291937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7292476Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:55:43.7292742Z         
2025-03-14T04:55:43.7293098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7293837Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7294382Z         
2025-03-14T04:55:43.7294728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7296583Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7298231Z         
2025-03-14T04:55:43.7298607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7299094Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:55:43.7299362Z         
2025-03-14T04:55:43.7299697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7300434Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7300975Z         
2025-03-14T04:55:43.7301330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7303182Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7305026Z         
2025-03-14T04:55:43.7305447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7305977Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:55:43.7306252Z         
2025-03-14T04:55:43.7306606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7307415Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7307992Z         
2025-03-14T04:55:43.7308363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7310265Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7311883Z         
2025-03-14T04:55:43.7312268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7312761Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:55:43.7313035Z         
2025-03-14T04:55:43.7313407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7313899Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:55:43.7314161Z         
2025-03-14T04:55:43.7314497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7315227Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7315764Z         
2025-03-14T04:55:43.7316116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7317992Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7319621Z         
2025-03-14T04:55:43.7319993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7320486Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:55:43.7320742Z         
2025-03-14T04:55:43.7321079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7321804Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7322348Z         
2025-03-14T04:55:43.7322695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7324541Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7326166Z         
2025-03-14T04:55:43.7326539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7327021Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:55:43.7327283Z         
2025-03-14T04:55:43.7327626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7328354Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7328899Z         
2025-03-14T04:55:43.7329258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7331130Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7332767Z         
2025-03-14T04:55:43.7333136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7333625Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:55:43.7333895Z         
2025-03-14T04:55:43.7334287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7334770Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:55:43.7335035Z         
2025-03-14T04:55:43.7335396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7336126Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7336664Z         
2025-03-14T04:55:43.7337016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7338905Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7340547Z         
2025-03-14T04:55:43.7340920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7341401Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:55:43.7341662Z         
2025-03-14T04:55:43.7341988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7342731Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7343271Z         
2025-03-14T04:55:43.7343620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7345667Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7347436Z         
2025-03-14T04:55:43.7347836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7348368Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:55:43.7348649Z         
2025-03-14T04:55:43.7349006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7349791Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7350375Z         
2025-03-14T04:55:43.7350749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7352728Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7354473Z         
2025-03-14T04:55:43.7354854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7355375Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:55:43.7355653Z         
2025-03-14T04:55:43.7356048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7356552Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:55:43.7356817Z         
2025-03-14T04:55:43.7357157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7357890Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7358431Z         
2025-03-14T04:55:43.7358792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7360649Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7362306Z         
2025-03-14T04:55:43.7362678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7363157Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:55:43.7363416Z         
2025-03-14T04:55:43.7363753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7364485Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7365025Z         
2025-03-14T04:55:43.7365373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7367240Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7368859Z         
2025-03-14T04:55:43.7369235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7369715Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:55:43.7369974Z         
2025-03-14T04:55:43.7370313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7371053Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7371594Z         
2025-03-14T04:55:43.7371944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7373826Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7375459Z         
2025-03-14T04:55:43.7375823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7376335Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:55:43.7376609Z         
2025-03-14T04:55:43.7376984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7377471Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:55:43.7377734Z         
2025-03-14T04:55:43.7378075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7378799Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7379330Z         
2025-03-14T04:55:43.7379683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7381671Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7383399Z         
2025-03-14T04:55:43.7383789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7384388Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:55:43.7384672Z         
2025-03-14T04:55:43.7385058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7385836Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7386409Z         
2025-03-14T04:55:43.7386804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7388721Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7390463Z         
2025-03-14T04:55:43.7390859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7391365Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:55:43.7391638Z         
2025-03-14T04:55:43.7391991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7392767Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7393341Z         
2025-03-14T04:55:43.7393710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7395667Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7397380Z         
2025-03-14T04:55:43.7397768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7398280Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:55:43.7398564Z         
2025-03-14T04:55:43.7398949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7399462Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:55:43.7399738Z         
2025-03-14T04:55:43.7400127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7400871Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7401411Z         
2025-03-14T04:55:43.7401763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7403601Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7405266Z         
2025-03-14T04:55:43.7405638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7406122Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:55:43.7406382Z         
2025-03-14T04:55:43.7406713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7407451Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7407989Z         
2025-03-14T04:55:43.7408341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7410213Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7411842Z         
2025-03-14T04:55:43.7412238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7412743Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:55:43.7413004Z         
2025-03-14T04:55:43.7413339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7414078Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7414632Z         
2025-03-14T04:55:43.7414997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7416844Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7418595Z         
2025-03-14T04:55:43.7418971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7419473Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:55:43.7419758Z         
2025-03-14T04:55:43.7420151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7420668Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:55:43.7420945Z         
2025-03-14T04:55:43.7421303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7422074Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7422641Z         
2025-03-14T04:55:43.7423011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7425102Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7426891Z         
2025-03-14T04:55:43.7427292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7427803Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:55:43.7428086Z         
2025-03-14T04:55:43.7428451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7429247Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7429818Z         
2025-03-14T04:55:43.7430193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7432139Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7433879Z         
2025-03-14T04:55:43.7434278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7434792Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:55:43.7435056Z         
2025-03-14T04:55:43.7435396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7436139Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7436685Z         
2025-03-14T04:55:43.7437035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7438920Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7440654Z         
2025-03-14T04:55:43.7441019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7441513Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:55:43.7441785Z         
2025-03-14T04:55:43.7442166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7442677Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:55:43.7442952Z         
2025-03-14T04:55:43.7443319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7444092Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7444637Z         
2025-03-14T04:55:43.7444985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7446818Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7448453Z         
2025-03-14T04:55:43.7448827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7449298Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:55:43.7449559Z         
2025-03-14T04:55:43.7449893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7450621Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7451165Z         
2025-03-14T04:55:43.7451514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7453376Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7455023Z         
2025-03-14T04:55:43.7455403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7455884Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:55:43.7456143Z         
2025-03-14T04:55:43.7456504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7457264Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7457813Z         
2025-03-14T04:55:43.7458170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7460166Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7461901Z         
2025-03-14T04:55:43.7462289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7462807Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:55:43.7463091Z         
2025-03-14T04:55:43.7463477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7463996Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:55:43.7464358Z         
2025-03-14T04:55:43.7464744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7465556Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7466133Z         
2025-03-14T04:55:43.7466520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7468466Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7470195Z         
2025-03-14T04:55:43.7470588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7471096Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:55:43.7471366Z         
2025-03-14T04:55:43.7471741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7472505Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7473114Z         
2025-03-14T04:55:43.7473482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7475006Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7475098Z         
2025-03-14T04:55:43.7475387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7475534Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:55:43.7475600Z         
2025-03-14T04:55:43.7475860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7476285Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7476360Z         
2025-03-14T04:55:43.7476632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7478169Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7478250Z         
2025-03-14T04:55:43.7478528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7478689Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:55:43.7478755Z         
2025-03-14T04:55:43.7479041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7479196Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:55:43.7479271Z         
2025-03-14T04:55:43.7479517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7479957Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7480031Z         
2025-03-14T04:55:43.7480297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7481975Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7482101Z         
2025-03-14T04:55:43.7482399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7482547Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:55:43.7482615Z         
2025-03-14T04:55:43.7482872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7483298Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7483393Z         
2025-03-14T04:55:43.7483659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7485197Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7485275Z         
2025-03-14T04:55:43.7485559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7485703Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:55:43.7485769Z         
2025-03-14T04:55:43.7486049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7486471Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7486565Z         
2025-03-14T04:55:43.7486830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7488349Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7488440Z         
2025-03-14T04:55:43.7488720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7488876Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:55:43.7488944Z         
2025-03-14T04:55:43.7489230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7489369Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:55:43.7489442Z         
2025-03-14T04:55:43.7489688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7490125Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7490193Z         
2025-03-14T04:55:43.7490467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7491999Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7492073Z         
2025-03-14T04:55:43.7492366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7492523Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:55:43.7492595Z         
2025-03-14T04:55:43.7492843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7493306Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7493373Z         
2025-03-14T04:55:43.7493644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7495162Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7495238Z         
2025-03-14T04:55:43.7495525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7495666Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:55:43.7495740Z         
2025-03-14T04:55:43.7495986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7496433Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7496502Z         
2025-03-14T04:55:43.7496770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7498297Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7498369Z         
2025-03-14T04:55:43.7498656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7498813Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:55:43.7498915Z         
2025-03-14T04:55:43.7499197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7499352Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:55:43.7499430Z         
2025-03-14T04:55:43.7499688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7500112Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7500200Z         
2025-03-14T04:55:43.7500465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7501988Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7502063Z         
2025-03-14T04:55:43.7502351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7502497Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:55:43.7502564Z         
2025-03-14T04:55:43.7502821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7503256Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7503331Z         
2025-03-14T04:55:43.7503592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7505323Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7505417Z         
2025-03-14T04:55:43.7505746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7505901Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:55:43.7505969Z         
2025-03-14T04:55:43.7506247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7506714Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7506792Z         
2025-03-14T04:55:43.7507067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7508695Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7508776Z         
2025-03-14T04:55:43.7509072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7509252Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:55:43.7509321Z         
2025-03-14T04:55:43.7509624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7509779Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:55:43.7509858Z         
2025-03-14T04:55:43.7510136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7510589Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7510660Z         
2025-03-14T04:55:43.7510944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7512562Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7512635Z         
2025-03-14T04:55:43.7512943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7513103Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:55:43.7513181Z         
2025-03-14T04:55:43.7513448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7513901Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7513992Z         
2025-03-14T04:55:43.7514272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7515875Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7515947Z         
2025-03-14T04:55:43.7516253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7516397Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:55:43.7516477Z         
2025-03-14T04:55:43.7516758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7517217Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7517294Z         
2025-03-14T04:55:43.7517571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7519143Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7519212Z         
2025-03-14T04:55:43.7519521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7519690Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:55:43.7519772Z         
2025-03-14T04:55:43.7520062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7520210Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:55:43.7520283Z         
2025-03-14T04:55:43.7520531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7520969Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7521033Z         
2025-03-14T04:55:43.7521299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7522837Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7522911Z         
2025-03-14T04:55:43.7523200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7523366Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:55:43.7523439Z         
2025-03-14T04:55:43.7523687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7524120Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7524187Z         
2025-03-14T04:55:43.7524456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7525992Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7526067Z         
2025-03-14T04:55:43.7526360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7526514Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:55:43.7526584Z         
2025-03-14T04:55:43.7526834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7527273Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7527352Z         
2025-03-14T04:55:43.7527620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7529128Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7529202Z         
2025-03-14T04:55:43.7529485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7529645Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:55:43.7529716Z         
2025-03-14T04:55:43.7530018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7530170Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:55:43.7530235Z         
2025-03-14T04:55:43.7530495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7530915Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7530989Z         
2025-03-14T04:55:43.7531251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7532801Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7532894Z         
2025-03-14T04:55:43.7533182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7533326Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:55:43.7533393Z         
2025-03-14T04:55:43.7533652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7534087Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7534163Z         
2025-03-14T04:55:43.7534426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7535933Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7536009Z         
2025-03-14T04:55:43.7536289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7536446Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:55:43.7536512Z         
2025-03-14T04:55:43.7536765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7537186Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7537261Z         
2025-03-14T04:55:43.7537525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7539067Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7539142Z         
2025-03-14T04:55:43.7539437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7539600Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:55:43.7539666Z         
2025-03-14T04:55:43.7539954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7540100Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:55:43.7540188Z         
2025-03-14T04:55:43.7540437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7540853Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7540919Z         
2025-03-14T04:55:43.7541200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7542794Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7542880Z         
2025-03-14T04:55:43.7543186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7543329Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:55:43.7543408Z         
2025-03-14T04:55:43.7543668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7544178Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7544261Z         
2025-03-14T04:55:43.7544570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7546250Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7546337Z         
2025-03-14T04:55:43.7546645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7546788Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:55:43.7546866Z         
2025-03-14T04:55:43.7547128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7547619Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7547690Z         
2025-03-14T04:55:43.7547976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7549583Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7549657Z         
2025-03-14T04:55:43.7549950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7550415Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7550492Z         
2025-03-14T04:55:43.7550769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7552502Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7552584Z         
2025-03-14T04:55:43.7552902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7553061Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:55:43.7553143Z         
2025-03-14T04:55:43.7553436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7553585Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:55:43.7553659Z         
2025-03-14T04:55:43.7553923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7554354Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7554427Z         
2025-03-14T04:55:43.7554686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7556208Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7556275Z         
2025-03-14T04:55:43.7556569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7556721Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:55:43.7556793Z         
2025-03-14T04:55:43.7557039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7557473Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7557544Z         
2025-03-14T04:55:43.7557807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7559337Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7559404Z         
2025-03-14T04:55:43.7559698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7559859Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:55:43.7559927Z         
2025-03-14T04:55:43.7560182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7560608Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7560699Z         
2025-03-14T04:55:43.7560964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7562468Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7562546Z         
2025-03-14T04:55:43.7562827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7562993Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:55:43.7563060Z         
2025-03-14T04:55:43.7563367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7563518Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:55:43.7563592Z         
2025-03-14T04:55:43.7563843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7564270Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7564337Z         
2025-03-14T04:55:43.7564608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7566141Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7566234Z         
2025-03-14T04:55:43.7566529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7566666Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:55:43.7566739Z         
2025-03-14T04:55:43.7566990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7567436Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7567502Z         
2025-03-14T04:55:43.7567773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7569290Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7569367Z         
2025-03-14T04:55:43.7569661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7569825Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:55:43.7569898Z         
2025-03-14T04:55:43.7570146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7570576Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7570642Z         
2025-03-14T04:55:43.7570908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7572453Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7572529Z         
2025-03-14T04:55:43.7572817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7572997Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:55:43.7573070Z         
2025-03-14T04:55:43.7573348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7573503Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:55:43.7573588Z         
2025-03-14T04:55:43.7574035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.7574187Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.7574261Z         
2025-03-14T04:55:43.7574555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.7574706Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:43.7574772Z         
2025-03-14T04:55:43.7575214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.7575368Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.7575441Z         
2025-03-14T04:55:43.7575730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.7575878Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:55:43.7575944Z         
2025-03-14T04:55:43.7576339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:43.7576518Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:55:43.7576625Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:55:43.7576747Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:55:43.7576823Z         
2025-03-14T04:55:43.7577154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:43.7577290Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:55:43.7577354Z         
2025-03-14T04:55:43.7577686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:43.7577805Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:55:43.7577878Z         
2025-03-14T04:55:43.7578259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:43.7578498Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:55:43.7578570Z         
2025-03-14T04:55:43.7578986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:43.7579134Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:55:43.7579558Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:55:43.7579688Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:55:43.7579804Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:55:43.7579893Z         
2025-03-14T04:55:43.7580194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.7580329Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:55:43.7580394Z         
2025-03-14T04:55:43.7580660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7581592Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:55:43.7581680Z         
2025-03-14T04:55:43.7581971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:43.7582179Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:55:43.7582248Z         
2025-03-14T04:55:43.7582652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:43.7583588Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:55:43.7583660Z         
2025-03-14T04:55:43.7584046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:43.7584925Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:55:43.7585008Z         
2025-03-14T04:55:43.7585380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:55:43.7585548Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:55:43.7585691Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:55:43.7585788Z         
2025-03-14T04:55:43.7586228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:55:43.7586396Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:55:43.7586578Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:55:43.7586778Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:55:43.7586854Z         
2025-03-14T04:55:43.7587256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:43.7587473Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:55:43.7587543Z         
2025-03-14T04:55:43.7587992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:43.7588147Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:55:43.7588311Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:43.7588454Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:43.7588528Z         
2025-03-14T04:55:43.7588910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:43.7589093Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:43.7589160Z         
2025-03-14T04:55:43.7589507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:43.7589651Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:43.7589725Z         
2025-03-14T04:55:43.7590044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:43.7590190Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:43.7590321Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.7590477Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:55:43.7590545Z         
2025-03-14T04:55:43.7590879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:43.7591007Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:43.7591139Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:43.7591288Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:55:43.7591365Z         
2025-03-14T04:55:43.7591696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:43.7591831Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.7591921Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:55:43.7592073Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:55:43.7592140Z         
2025-03-14T04:55:43.7592467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:43.7592615Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:43.7592719Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:55:43.7592849Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:55:43.7592940Z         
2025-03-14T04:55:43.7593308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:43.7593476Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:43.7593592Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:55:43.7593669Z         
2025-03-14T04:55:43.7594241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:43.7594398Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:43.7594524Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:55:43.7594592Z         
2025-03-14T04:55:43.7594909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:43.7595066Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:43.7595198Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:55:43.7595265Z         
2025-03-14T04:55:43.7595585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:43.7595791Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:43.7595913Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:55:43.7595981Z         
2025-03-14T04:55:43.7596338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:43.7596484Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:43.7596559Z         
2025-03-14T04:55:43.7596902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:43.7597047Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:43.7597115Z         
2025-03-14T04:55:43.7597472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:43.7597614Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:43.7597750Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:55:43.7597929Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:43.7598084Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:55:43.7598153Z         
2025-03-14T04:55:43.7598521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:43.7598682Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:43.7598817Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:55:43.7598981Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:43.7599127Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:55:43.7599207Z         
2025-03-14T04:55:43.7599545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:43.7599664Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:43.7599834Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:43.7599968Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:55:43.7600041Z         
2025-03-14T04:55:43.7600375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:43.7600499Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:43.7600673Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:43.7600811Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:55:43.7600885Z         
2025-03-14T04:55:43.7601200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:43.7601308Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:43.7601425Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:43.7601497Z         
2025-03-14T04:55:43.7601821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:43.7601926Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:43.7602039Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:43.7602114Z         
2025-03-14T04:55:43.7602421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:43.7602542Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:43.7602667Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:43.7602742Z         
2025-03-14T04:55:43.7603047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:43.7603167Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:43.7603291Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:43.7603366Z         
2025-03-14T04:55:43.7603719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:43.7603926Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:43.7603992Z         
2025-03-14T04:55:43.7604334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:43.7604511Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:55:43.7604583Z         
2025-03-14T04:55:43.7604968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:43.7605149Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:43.7605231Z         
2025-03-14T04:55:43.7605721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:43.7605857Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:43.7605931Z         
2025-03-14T04:55:43.7606224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.7606374Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:55:43.7606439Z         
2025-03-14T04:55:43.7606878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:43.7606995Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:55:43.7607111Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:55:43.7607226Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:43.7607301Z         
2025-03-14T04:55:43.7607764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:43.7607952Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:43.7608185Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:55:43.7608259Z         
2025-03-14T04:55:43.7608716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:43.7608885Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:43.7608961Z         
2025-03-14T04:55:43.7609252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.7609412Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:55:43.7609478Z         
2025-03-14T04:55:43.7609857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:43.7610004Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:55:43.7610080Z         
2025-03-14T04:55:43.7610387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.7610540Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:55:43.7610606Z         
2025-03-14T04:55:43.7611001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:43.7611142Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:55:43.7611216Z         
2025-03-14T04:55:43.7611690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:43.7611850Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:55:43.7611970Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:43.7612130Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:43.7612260Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:43.7612335Z         
2025-03-14T04:55:43.7612700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:43.7612825Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:43.7612890Z         
2025-03-14T04:55:43.7612901Z 
2025-03-14T04:55:43.7613011Z class GraphModule(torch.nn.Module):
2025-03-14T04:55:43.7714129Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", 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L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:55:43.7715027Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:55:43.7715365Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7715761Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7716182Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7716595Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7716978Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7717347Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7717832Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7718268Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7718692Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7719088Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7719439Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7719893Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7720307Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7720704Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7721099Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7721464Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7721882Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7722302Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7722713Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7723104Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7723473Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.7723911Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7724336Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7724738Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.7725127Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.7725444Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7725908Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7726317Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7726730Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7727114Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7727455Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7727863Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7728276Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7728676Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7729057Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7729386Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7729795Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7730175Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7730555Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7730908Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7731224Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7731600Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7731972Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7732328Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7732669Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7732992Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7733368Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7733751Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7734105Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7734453Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7734785Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7735159Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7735532Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7735884Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7736230Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7736549Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7736916Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7737307Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7737657Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7738016Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7738341Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7738711Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7739086Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7739432Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7739798Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7740107Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7740488Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7740871Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7741233Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7741592Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7741919Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.7742307Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7742683Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7743054Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.7743406Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.7743720Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7744196Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7744682Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7745097Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7745455Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7745776Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7746154Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7746556Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7746935Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7747327Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7747666Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7748092Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7748502Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7748879Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7749271Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7749610Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7750029Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7750429Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7750825Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7751200Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7751537Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7751973Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7752375Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7752764Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7753139Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7753466Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7753843Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7754225Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7754608Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7754958Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7755303Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7755683Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7756065Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7756447Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7756804Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7757123Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7757514Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7757895Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7758256Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7758613Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7758946Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7759337Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7759714Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7760105Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7760450Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7760779Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7761162Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7761535Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7761916Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7762263Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7762606Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7762987Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7763377Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7763748Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7764114Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7764442Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7764821Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7765206Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7765558Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7765904Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7766259Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.7766648Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7767026Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7767395Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.7767757Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.7768067Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7768440Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7768820Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7769177Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7769534Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7769857Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7770222Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7770618Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7770970Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7771321Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7771635Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7772001Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7772384Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7772729Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7773104Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7773413Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7773788Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7774170Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7774516Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7774874Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7775305Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7775771Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7776159Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7776559Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7776936Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7777520Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7777922Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7778334Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7778683Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7779039Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7779417Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7779804Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7780205Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7780553Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7780926Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7781261Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7781797Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7782177Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7782573Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7782943Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7783296Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7783782Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7784196Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7784642Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7785017Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7785410Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7785884Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7786278Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7786690Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7787041Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7787392Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7787786Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7788189Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7788575Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7788954Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7789309Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7789716Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7790128Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7790492Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7790882Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7791208Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7791668Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7792076Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7792449Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7792824Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7793132Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7793551Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7793910Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7794275Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7794606Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7794926Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7795340Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7795711Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7796075Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7796405Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7796732Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7797105Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7797504Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7797864Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7798198Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7798559Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7798940Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7799358Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7799699Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7800064Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7800400Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7800795Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7801194Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7801531Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7801896Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7802197Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7802591Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7802971Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7803348Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7803709Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7804013Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7804410Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7804777Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7805162Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7805518Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7805853Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7806215Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7806635Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7806996Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7807341Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7807670Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7808026Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7808445Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7808782Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7809143Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7809445Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7809843Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7810258Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7810593Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7810953Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7811257Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7811644Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7812034Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7812432Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7812809Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7813122Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7813512Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7813880Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7814267Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7814599Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7814970Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7815361Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7815742Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7816117Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7816443Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7835938Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7836517Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7836874Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7837214Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7837525Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7837830Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7838178Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7838535Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7838891Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7839216Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7839534Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7839888Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7840236Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7840586Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7840910Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7841200Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7841549Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7841887Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7842224Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7842538Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7842850Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7843198Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7843536Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7843869Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7844182Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7844482Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7844820Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7845180Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7845513Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7846192Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7846494Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7846848Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7847203Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7847534Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7847851Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7848146Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7848484Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7848830Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7849152Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7849490Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7849781Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7850117Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7850461Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7850780Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7851100Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7851387Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7851731Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7852085Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7852413Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7852751Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7853041Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7853394Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7853747Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7854073Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7854388Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7854683Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7855028Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7855374Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7855703Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7856029Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7856325Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7856669Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7857013Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7857336Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7857656Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7857940Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7858303Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7858646Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7858981Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7859301Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7859585Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7859963Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7860308Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7860643Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7860959Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7861258Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7861614Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7861957Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7862308Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7862622Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7862919Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7863263Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7863617Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7863956Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7864393Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7864735Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7865100Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7865493Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7865816Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7866136Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7866437Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7866788Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7867132Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7867467Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7867790Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7868079Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7868428Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7868785Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7869115Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7869428Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7869725Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7870066Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7870415Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7870750Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7871081Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7871374Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7871716Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7872080Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7872403Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7872768Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7873053Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7873406Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7873752Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7874072Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7874393Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7874679Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7875028Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7875388Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7875715Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7876030Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7876326Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7876672Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7877004Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7877331Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7877656Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7877951Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7878310Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7878660Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7878995Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7879312Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7879604Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7879944Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7880286Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7880610Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7880926Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7881210Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7881883Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7882231Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7882565Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7882889Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7883186Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7883549Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7883885Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7884242Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7884552Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7884885Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7885230Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7885575Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7885928Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7886238Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7886535Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7886875Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7887224Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7887549Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7887871Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7888174Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7888526Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7888874Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7889198Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7889519Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7889812Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7890161Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7890517Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7890848Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7891181Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7891467Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7891821Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7892175Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7892500Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7892813Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7893107Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7893448Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7893797Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7894126Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7894452Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7894748Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7895091Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7895441Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7895761Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7896098Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7896382Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7896743Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7897091Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7897412Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7897744Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7898028Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7898396Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7898739Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7899072Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7899385Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7899681Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7900035Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7900378Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7900708Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7901036Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7901329Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7901676Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7902025Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7902353Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7902674Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7902972Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7903331Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7903678Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7904027Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7904426Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7904736Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7905124Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7905483Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7905846Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7906174Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7906469Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7906897Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7907249Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7907614Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7907945Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7908277Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:55:43.7908654Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7909032Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7909397Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:55:43.7909744Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:55:43.7910070Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7910430Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7910810Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7911151Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7911489Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7911811Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7912242Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7912611Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7912951Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7913291Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7913593Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7913958Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7914334Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7914681Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7915016Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7915319Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:55:43.7915684Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7916039Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7916382Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:55:43.7916727Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:55:43.7917037Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:55:43.7917395Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7917779Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7918125Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:55:43.7918468Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:55:43.7918776Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:55:43.7919137Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:55:43.7919493Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:55:43.7919820Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:55:43.7920147Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:55:43.7920503Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:55:43.7920842Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:55:43.7921184Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:55:43.7921565Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:55:43.7921938Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:55:43.7922296Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:55:43.7922656Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:55:43.7922737Z         
2025-03-14T04:55:43.7923064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7923576Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7923659Z         
2025-03-14T04:55:43.7923953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7925467Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7925561Z         
2025-03-14T04:55:43.7925864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:55:43.7926020Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:55:43.7926087Z         
2025-03-14T04:55:43.7926472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:55:43.7926719Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:55:43.7926797Z         
2025-03-14T04:55:43.7927066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7927512Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7927581Z         
2025-03-14T04:55:43.7927884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7929458Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7929529Z         
2025-03-14T04:55:43.7929838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7929986Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:55:43.7930063Z         
2025-03-14T04:55:43.7930327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7930793Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7930880Z         
2025-03-14T04:55:43.7931155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7932669Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7932752Z         
2025-03-14T04:55:43.7933051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7933193Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:55:43.7933268Z         
2025-03-14T04:55:43.7933516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7933961Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7934038Z         
2025-03-14T04:55:43.7934300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7935846Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7935919Z         
2025-03-14T04:55:43.7936183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7936619Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7936694Z         
2025-03-14T04:55:43.7936968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7938532Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7938638Z         
2025-03-14T04:55:43.7938926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7939082Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:55:43.7939148Z         
2025-03-14T04:55:43.7939439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7939594Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:55:43.7939666Z         
2025-03-14T04:55:43.7939916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7940345Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7940419Z         
2025-03-14T04:55:43.7940683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7942205Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7942275Z         
2025-03-14T04:55:43.7942581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7942740Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:55:43.7942809Z         
2025-03-14T04:55:43.7943069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7943528Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7943607Z         
2025-03-14T04:55:43.7943909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7945653Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7945771Z         
2025-03-14T04:55:43.7946080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7946240Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:55:43.7946322Z         
2025-03-14T04:55:43.7946591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7947023Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7947101Z         
2025-03-14T04:55:43.7947369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7948936Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7949014Z         
2025-03-14T04:55:43.7949299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7949462Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:55:43.7949530Z         
2025-03-14T04:55:43.7949819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7949970Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:55:43.7950043Z         
2025-03-14T04:55:43.7950291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7950733Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7950801Z         
2025-03-14T04:55:43.7951073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7952592Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7952686Z         
2025-03-14T04:55:43.7952979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7953120Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:55:43.7953193Z         
2025-03-14T04:55:43.7953442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7953873Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7953940Z         
2025-03-14T04:55:43.7954214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7955745Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7955823Z         
2025-03-14T04:55:43.7956115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7956252Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:55:43.7956326Z         
2025-03-14T04:55:43.7956573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7957006Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7957089Z         
2025-03-14T04:55:43.7957361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7958887Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7958998Z         
2025-03-14T04:55:43.7959286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7959442Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:55:43.7959517Z         
2025-03-14T04:55:43.7959802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7959961Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:55:43.7960028Z         
2025-03-14T04:55:43.7960286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7960712Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7960787Z         
2025-03-14T04:55:43.7961053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7962578Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7962656Z         
2025-03-14T04:55:43.7962944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7963095Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:55:43.7963160Z         
2025-03-14T04:55:43.7963417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7963863Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7963936Z         
2025-03-14T04:55:43.7964199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7965722Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7965810Z         
2025-03-14T04:55:43.7966094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7966246Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:55:43.7966311Z         
2025-03-14T04:55:43.7966565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7966997Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7967070Z         
2025-03-14T04:55:43.7967334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7968861Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7968934Z         
2025-03-14T04:55:43.7969185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7969635Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.7969700Z         
2025-03-14T04:55:43.7969970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7971556Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7971651Z         
2025-03-14T04:55:43.7971942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7972091Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:55:43.7972163Z         
2025-03-14T04:55:43.7972444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7972604Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:55:43.7972672Z         
2025-03-14T04:55:43.7972930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7973352Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7973429Z         
2025-03-14T04:55:43.7973692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7975242Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7975321Z         
2025-03-14T04:55:43.7975608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7975759Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:55:43.7975825Z         
2025-03-14T04:55:43.7976081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7976506Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7976582Z         
2025-03-14T04:55:43.7976868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7978392Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7978528Z         
2025-03-14T04:55:43.7978812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7978960Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:55:43.7979025Z         
2025-03-14T04:55:43.7979280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7979704Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7979777Z         
2025-03-14T04:55:43.7980044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7981738Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7981824Z         
2025-03-14T04:55:43.7982106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7982274Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:55:43.7982343Z         
2025-03-14T04:55:43.7982636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7982789Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:55:43.7982864Z         
2025-03-14T04:55:43.7983113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7983565Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7983639Z         
2025-03-14T04:55:43.7983901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7985661Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7985765Z         
2025-03-14T04:55:43.7986086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7986240Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:55:43.7986306Z         
2025-03-14T04:55:43.7986566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7986994Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7987070Z         
2025-03-14T04:55:43.7987333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7988862Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7988939Z         
2025-03-14T04:55:43.7989227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7989378Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:55:43.7989446Z         
2025-03-14T04:55:43.7989704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7990130Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.7990221Z         
2025-03-14T04:55:43.7990486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7992008Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7992112Z         
2025-03-14T04:55:43.7992400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.7992564Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:55:43.7992632Z         
2025-03-14T04:55:43.7992928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7993079Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:55:43.7993154Z         
2025-03-14T04:55:43.7993407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7993841Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.7993909Z         
2025-03-14T04:55:43.7994187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7995722Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7995797Z         
2025-03-14T04:55:43.7996086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7996225Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:55:43.7996297Z         
2025-03-14T04:55:43.7996544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.7996996Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.7997063Z         
2025-03-14T04:55:43.7997334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.7998872Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.7998959Z         
2025-03-14T04:55:43.7999252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.7999393Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:55:43.7999467Z         
2025-03-14T04:55:43.7999713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8000152Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8000219Z         
2025-03-14T04:55:43.8000490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8002051Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8002133Z         
2025-03-14T04:55:43.8002433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8002597Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:55:43.8002673Z         
2025-03-14T04:55:43.8002970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8003133Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:55:43.8003203Z         
2025-03-14T04:55:43.8003485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8003925Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8004023Z         
2025-03-14T04:55:43.8004301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8005895Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8005987Z         
2025-03-14T04:55:43.8006289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8006441Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:55:43.8006511Z         
2025-03-14T04:55:43.8006788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8007271Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8007349Z         
2025-03-14T04:55:43.8007627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8009250Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8009349Z         
2025-03-14T04:55:43.8009653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8009803Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:55:43.8009870Z         
2025-03-14T04:55:43.8010139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8010606Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8010687Z         
2025-03-14T04:55:43.8010965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8012562Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8012659Z         
2025-03-14T04:55:43.8012920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8013379Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.8013449Z         
2025-03-14T04:55:43.8013734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8015301Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8015370Z         
2025-03-14T04:55:43.8015660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8015800Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:55:43.8015875Z         
2025-03-14T04:55:43.8016158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8016312Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:55:43.8016377Z         
2025-03-14T04:55:43.8016634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8017063Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8017136Z         
2025-03-14T04:55:43.8017397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8018944Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8019040Z         
2025-03-14T04:55:43.8019341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8019493Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:55:43.8019560Z         
2025-03-14T04:55:43.8019836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8020277Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8020355Z         
2025-03-14T04:55:43.8020637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8022240Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8022322Z         
2025-03-14T04:55:43.8022622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8022772Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:55:43.8022840Z         
2025-03-14T04:55:43.8023110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8023550Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8023629Z         
2025-03-14T04:55:43.8023938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8025675Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8025798Z         
2025-03-14T04:55:43.8026111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8026295Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:55:43.8026367Z         
2025-03-14T04:55:43.8026671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8026823Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:55:43.8026900Z         
2025-03-14T04:55:43.8027165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8027610Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8027688Z         
2025-03-14T04:55:43.8027965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8029598Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8029669Z         
2025-03-14T04:55:43.8029979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8030129Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:55:43.8030197Z         
2025-03-14T04:55:43.8030460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8030922Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8031002Z         
2025-03-14T04:55:43.8031283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8032890Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8032982Z         
2025-03-14T04:55:43.8033285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8033436Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:55:43.8033505Z         
2025-03-14T04:55:43.8033777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8034219Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8034299Z         
2025-03-14T04:55:43.8034577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8036203Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8036285Z         
2025-03-14T04:55:43.8036578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8036740Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:55:43.8036809Z         
2025-03-14T04:55:43.8037113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8037262Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:55:43.8037338Z         
2025-03-14T04:55:43.8037601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8038067Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8038133Z         
2025-03-14T04:55:43.8038419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8039919Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8040012Z         
2025-03-14T04:55:43.8040304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8040439Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:55:43.8040512Z         
2025-03-14T04:55:43.8040761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8041189Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8041254Z         
2025-03-14T04:55:43.8041523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8043040Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8043115Z         
2025-03-14T04:55:43.8043411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8043546Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:55:43.8043620Z         
2025-03-14T04:55:43.8043871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8044317Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8044385Z         
2025-03-14T04:55:43.8044662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8046174Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8046269Z         
2025-03-14T04:55:43.8046559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8046708Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:55:43.8046781Z         
2025-03-14T04:55:43.8047066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8047215Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:55:43.8047283Z         
2025-03-14T04:55:43.8047542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8047959Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8048033Z         
2025-03-14T04:55:43.8048295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8049847Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8049930Z         
2025-03-14T04:55:43.8050237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8050378Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:55:43.8050444Z         
2025-03-14T04:55:43.8050698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8051129Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8051204Z         
2025-03-14T04:55:43.8051478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8052976Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8053064Z         
2025-03-14T04:55:43.8053355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8053496Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:55:43.8053562Z         
2025-03-14T04:55:43.8053821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8054241Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8054312Z         
2025-03-14T04:55:43.8054580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8056106Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8056184Z         
2025-03-14T04:55:43.8056460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8056615Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:55:43.8056680Z         
2025-03-14T04:55:43.8056970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8057112Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:55:43.8057188Z         
2025-03-14T04:55:43.8057448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8057867Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8057948Z         
2025-03-14T04:55:43.8058220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8059723Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8059815Z         
2025-03-14T04:55:43.8060114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8060246Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:55:43.8060320Z         
2025-03-14T04:55:43.8060571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8060993Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8061060Z         
2025-03-14T04:55:43.8061334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8062917Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8062991Z         
2025-03-14T04:55:43.8063299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8063436Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:55:43.8063512Z         
2025-03-14T04:55:43.8063773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8064301Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8064376Z         
2025-03-14T04:55:43.8064678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8066329Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8066415Z         
2025-03-14T04:55:43.8066716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8066871Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:55:43.8066947Z         
2025-03-14T04:55:43.8067242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8067404Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:55:43.8067475Z         
2025-03-14T04:55:43.8067746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8068178Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8068259Z         
2025-03-14T04:55:43.8068560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8070145Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8070226Z         
2025-03-14T04:55:43.8070520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8070670Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:55:43.8070741Z         
2025-03-14T04:55:43.8071024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8071462Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8071557Z         
2025-03-14T04:55:43.8071846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8073428Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8073522Z         
2025-03-14T04:55:43.8073821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8073969Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:55:43.8074039Z         
2025-03-14T04:55:43.8074310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8074756Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8074826Z         
2025-03-14T04:55:43.8075112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8076686Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8076763Z         
2025-03-14T04:55:43.8077041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8077191Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:55:43.8077256Z         
2025-03-14T04:55:43.8077542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8077714Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:55:43.8077781Z         
2025-03-14T04:55:43.8078035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8078462Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8078534Z         
2025-03-14T04:55:43.8078797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8080308Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8080399Z         
2025-03-14T04:55:43.8080687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8080828Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:55:43.8080893Z         
2025-03-14T04:55:43.8081150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8081693Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8081777Z         
2025-03-14T04:55:43.8082090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8083598Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8083676Z         
2025-03-14T04:55:43.8083960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8084100Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:55:43.8084166Z         
2025-03-14T04:55:43.8084472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8084891Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8084998Z         
2025-03-14T04:55:43.8085262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8086760Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8086856Z         
2025-03-14T04:55:43.8087140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8087295Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:55:43.8087363Z         
2025-03-14T04:55:43.8087655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8087804Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:55:43.8087878Z         
2025-03-14T04:55:43.8088127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8088574Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8088642Z         
2025-03-14T04:55:43.8088913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8090432Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8090509Z         
2025-03-14T04:55:43.8090804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8090958Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:55:43.8091029Z         
2025-03-14T04:55:43.8091277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8091720Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8091785Z         
2025-03-14T04:55:43.8092056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8093556Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8093648Z         
2025-03-14T04:55:43.8093938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8094077Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:55:43.8094150Z         
2025-03-14T04:55:43.8094396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8094823Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8094905Z         
2025-03-14T04:55:43.8095174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8096693Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8096760Z         
2025-03-14T04:55:43.8097045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8097197Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:55:43.8097272Z         
2025-03-14T04:55:43.8097573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8097723Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:55:43.8097787Z         
2025-03-14T04:55:43.8098057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8098469Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8098542Z         
2025-03-14T04:55:43.8098805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8100337Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8100413Z         
2025-03-14T04:55:43.8100699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8100846Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:55:43.8100912Z         
2025-03-14T04:55:43.8101167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8101597Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8101672Z         
2025-03-14T04:55:43.8101935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8103536Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8103615Z         
2025-03-14T04:55:43.8103913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8104136Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:55:43.8104221Z         
2025-03-14T04:55:43.8104514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8104978Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8105055Z         
2025-03-14T04:55:43.8105329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8106948Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8107042Z         
2025-03-14T04:55:43.8107362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8107542Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:55:43.8107616Z         
2025-03-14T04:55:43.8107937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8108095Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:55:43.8108179Z         
2025-03-14T04:55:43.8108473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8108949Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8109023Z         
2025-03-14T04:55:43.8109331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8111046Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8111121Z         
2025-03-14T04:55:43.8111471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8111628Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:55:43.8111726Z         
2025-03-14T04:55:43.8112002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8112478Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8112558Z         
2025-03-14T04:55:43.8112867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8114516Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8114585Z         
2025-03-14T04:55:43.8114880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8115014Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:55:43.8115088Z         
2025-03-14T04:55:43.8115335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8115789Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8115863Z         
2025-03-14T04:55:43.8116129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8117661Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8117730Z         
2025-03-14T04:55:43.8118040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8118191Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:55:43.8118265Z         
2025-03-14T04:55:43.8118550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8118717Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:55:43.8118783Z         
2025-03-14T04:55:43.8119039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8119457Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8119537Z         
2025-03-14T04:55:43.8119809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8121335Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8121415Z         
2025-03-14T04:55:43.8121723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8121863Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:55:43.8121939Z         
2025-03-14T04:55:43.8122206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8122629Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8122695Z         
2025-03-14T04:55:43.8122965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8124464Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8124557Z         
2025-03-14T04:55:43.8124847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8124982Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:55:43.8125069Z         
2025-03-14T04:55:43.8125320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8125750Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8125831Z         
2025-03-14T04:55:43.8126104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8127611Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8127687Z         
2025-03-14T04:55:43.8127977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8128125Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:55:43.8128199Z         
2025-03-14T04:55:43.8128482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8128646Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:55:43.8128714Z         
2025-03-14T04:55:43.8128976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8129393Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8129467Z         
2025-03-14T04:55:43.8129726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8131251Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8131327Z         
2025-03-14T04:55:43.8131610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8131774Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:55:43.8131842Z         
2025-03-14T04:55:43.8132099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8132525Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8132613Z         
2025-03-14T04:55:43.8132880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8134395Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8134471Z         
2025-03-14T04:55:43.8134758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8134900Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:55:43.8134965Z         
2025-03-14T04:55:43.8135242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8135670Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8135748Z         
2025-03-14T04:55:43.8136011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8137569Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8137647Z         
2025-03-14T04:55:43.8137927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8138099Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:55:43.8138165Z         
2025-03-14T04:55:43.8138457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8138598Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:55:43.8138672Z         
2025-03-14T04:55:43.8138920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8139382Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8139451Z         
2025-03-14T04:55:43.8139738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8141343Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8141415Z         
2025-03-14T04:55:43.8141737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8141880Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:55:43.8141956Z         
2025-03-14T04:55:43.8142218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8142678Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8142748Z         
2025-03-14T04:55:43.8143039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8144793Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8144894Z         
2025-03-14T04:55:43.8145219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8145361Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:55:43.8145437Z         
2025-03-14T04:55:43.8145695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8146150Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8146239Z         
2025-03-14T04:55:43.8146526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8148146Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8148216Z         
2025-03-14T04:55:43.8148522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8148699Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:55:43.8148778Z         
2025-03-14T04:55:43.8149081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8149241Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:55:43.8149309Z         
2025-03-14T04:55:43.8149582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8150029Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8150103Z         
2025-03-14T04:55:43.8150393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8152004Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8152101Z         
2025-03-14T04:55:43.8152406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8152561Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:55:43.8152630Z         
2025-03-14T04:55:43.8152925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8153374Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8153445Z         
2025-03-14T04:55:43.8153731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8155325Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8155403Z         
2025-03-14T04:55:43.8155704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8155849Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:55:43.8155924Z         
2025-03-14T04:55:43.8156172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8156603Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8156669Z         
2025-03-14T04:55:43.8156939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8158463Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8158554Z         
2025-03-14T04:55:43.8158846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8158993Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:55:43.8159069Z         
2025-03-14T04:55:43.8159355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8159525Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:55:43.8159591Z         
2025-03-14T04:55:43.8159847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8160255Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8160331Z         
2025-03-14T04:55:43.8160592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8162123Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8162201Z         
2025-03-14T04:55:43.8162483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8162627Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:55:43.8162693Z         
2025-03-14T04:55:43.8162946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8163359Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8163431Z         
2025-03-14T04:55:43.8163692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8165243Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8165331Z         
2025-03-14T04:55:43.8165613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8165757Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:55:43.8165835Z         
2025-03-14T04:55:43.8166093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8166509Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8166583Z         
2025-03-14T04:55:43.8166847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8168358Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8168434Z         
2025-03-14T04:55:43.8168729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8168888Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:55:43.8168953Z         
2025-03-14T04:55:43.8169245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8169387Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:55:43.8169460Z         
2025-03-14T04:55:43.8169708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8170133Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8170199Z         
2025-03-14T04:55:43.8170472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8172026Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8172106Z         
2025-03-14T04:55:43.8172399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8172550Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:55:43.8172623Z         
2025-03-14T04:55:43.8172874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8173303Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8173367Z         
2025-03-14T04:55:43.8173642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8175186Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8175254Z         
2025-03-14T04:55:43.8175548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8175684Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:55:43.8175757Z         
2025-03-14T04:55:43.8176010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8176441Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8176510Z         
2025-03-14T04:55:43.8176787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8178365Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8178449Z         
2025-03-14T04:55:43.8178737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8178888Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:55:43.8178978Z         
2025-03-14T04:55:43.8179260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8179406Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:55:43.8179472Z         
2025-03-14T04:55:43.8179729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8180144Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8180222Z         
2025-03-14T04:55:43.8180486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8182407Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8182495Z         
2025-03-14T04:55:43.8182797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8182950Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:55:43.8183018Z         
2025-03-14T04:55:43.8183289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8183727Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8183803Z         
2025-03-14T04:55:43.8184079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8185846Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8185958Z         
2025-03-14T04:55:43.8186282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8186434Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:55:43.8186508Z         
2025-03-14T04:55:43.8186782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8187227Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8187304Z         
2025-03-14T04:55:43.8187584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8189225Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8189305Z         
2025-03-14T04:55:43.8189599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8189767Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:55:43.8189834Z         
2025-03-14T04:55:43.8190137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8190286Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:55:43.8190362Z         
2025-03-14T04:55:43.8190622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8191062Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8191140Z         
2025-03-14T04:55:43.8191430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8193046Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8193149Z         
2025-03-14T04:55:43.8193460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8193614Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:55:43.8193684Z         
2025-03-14T04:55:43.8193955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8194406Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8194484Z         
2025-03-14T04:55:43.8194763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8196343Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8196421Z         
2025-03-14T04:55:43.8196704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8196854Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:55:43.8196919Z         
2025-03-14T04:55:43.8197172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8197610Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8197683Z         
2025-03-14T04:55:43.8197945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8199470Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8199572Z         
2025-03-14T04:55:43.8199852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8200013Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:55:43.8200078Z         
2025-03-14T04:55:43.8200367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8200519Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:55:43.8200593Z         
2025-03-14T04:55:43.8200842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8201269Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8201337Z         
2025-03-14T04:55:43.8201606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8203129Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8203204Z         
2025-03-14T04:55:43.8203493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8203633Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:55:43.8203707Z         
2025-03-14T04:55:43.8203956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8204383Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8204467Z         
2025-03-14T04:55:43.8204743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8206251Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8206364Z         
2025-03-14T04:55:43.8206656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8206795Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:55:43.8206865Z         
2025-03-14T04:55:43.8207114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8207546Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8207615Z         
2025-03-14T04:55:43.8207888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8209434Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8209504Z         
2025-03-14T04:55:43.8209789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8209949Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:55:43.8210022Z         
2025-03-14T04:55:43.8210303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8210457Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:55:43.8210523Z         
2025-03-14T04:55:43.8210778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8211220Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8211295Z         
2025-03-14T04:55:43.8211555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8213158Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8213254Z         
2025-03-14T04:55:43.8213561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8213714Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:55:43.8213783Z         
2025-03-14T04:55:43.8214056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8214506Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8214585Z         
2025-03-14T04:55:43.8214864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8216502Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8216581Z         
2025-03-14T04:55:43.8216877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8217033Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:55:43.8217102Z         
2025-03-14T04:55:43.8217370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8217834Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8217912Z         
2025-03-14T04:55:43.8218187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8219821Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8219914Z         
2025-03-14T04:55:43.8220210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8220385Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:55:43.8220458Z         
2025-03-14T04:55:43.8220766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8220917Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:55:43.8220996Z         
2025-03-14T04:55:43.8221262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8221710Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8221786Z         
2025-03-14T04:55:43.8222083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8223691Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8223763Z         
2025-03-14T04:55:43.8224068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8224268Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:55:43.8224349Z         
2025-03-14T04:55:43.8224612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8225106Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8225211Z         
2025-03-14T04:55:43.8225515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8227134Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8227218Z         
2025-03-14T04:55:43.8227515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8227652Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:55:43.8227728Z         
2025-03-14T04:55:43.8227978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8228417Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8228496Z         
2025-03-14T04:55:43.8228761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8230311Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8230381Z         
2025-03-14T04:55:43.8230673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8230844Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:55:43.8230913Z         
2025-03-14T04:55:43.8231206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8231353Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:55:43.8231448Z         
2025-03-14T04:55:43.8231696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8232120Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8232199Z         
2025-03-14T04:55:43.8232471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8233992Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8234117Z         
2025-03-14T04:55:43.8234410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8234546Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:55:43.8234622Z         
2025-03-14T04:55:43.8234872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8235298Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8235366Z         
2025-03-14T04:55:43.8235659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8237176Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8237247Z         
2025-03-14T04:55:43.8237540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8237674Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:55:43.8237749Z         
2025-03-14T04:55:43.8238015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8238453Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8238535Z         
2025-03-14T04:55:43.8238809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8240332Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8240416Z         
2025-03-14T04:55:43.8240707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8240864Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:55:43.8240938Z         
2025-03-14T04:55:43.8241220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8241371Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:55:43.8241438Z         
2025-03-14T04:55:43.8241696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8242129Z         x_190: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8242206Z         
2025-03-14T04:55:43.8242469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8243989Z         x_191: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8244065Z         
2025-03-14T04:55:43.8244349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8244510Z         out_120: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:55:43.8244576Z         
2025-03-14T04:55:43.8244832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8245255Z         x_192: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_120 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8245342Z         
2025-03-14T04:55:43.8245606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8247119Z         x_193: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8247219Z         
2025-03-14T04:55:43.8247502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8247648Z         out_121: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:55:43.8247713Z         
2025-03-14T04:55:43.8247967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8248393Z         x_194: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8248470Z         
2025-03-14T04:55:43.8248747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8250263Z         x_195: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8250340Z         
2025-03-14T04:55:43.8250592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8251057Z         x_196: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:55:43.8251133Z         
2025-03-14T04:55:43.8251406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8252975Z         x_197: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8253057Z         
2025-03-14T04:55:43.8253348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8253502Z         x_195 += x_197;  out_122: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:55:43.8253580Z         
2025-03-14T04:55:43.8253862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8254018Z         out_123: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:55:43.8254085Z         
2025-03-14T04:55:43.8254349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8254778Z         x_198: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8254845Z         
2025-03-14T04:55:43.8255136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8256641Z         x_199: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8256720Z         
2025-03-14T04:55:43.8257003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8257149Z         out_124: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:55:43.8257215Z         
2025-03-14T04:55:43.8257469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8257915Z         x_200: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_124 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8257998Z         
2025-03-14T04:55:43.8258269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8259812Z         x_201: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8259918Z         
2025-03-14T04:55:43.8260224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8260380Z         out_125: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:55:43.8260457Z         
2025-03-14T04:55:43.8260724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8261189Z         x_202: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8261261Z         
2025-03-14T04:55:43.8261545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8263178Z         x_203: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8263261Z         
2025-03-14T04:55:43.8263557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8263725Z         x_203 += out_123;  out_126: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:55:43.8263801Z         
2025-03-14T04:55:43.8264151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8264330Z         out_127: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:55:43.8264420Z         
2025-03-14T04:55:43.8264689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8265129Z         x_204: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:55:43.8265226Z         
2025-03-14T04:55:43.8265509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8267106Z         x_205: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8267203Z         
2025-03-14T04:55:43.8267507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8267660Z         out_128: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:55:43.8267731Z         
2025-03-14T04:55:43.8268006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8268447Z         x_206: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_128 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:55:43.8268528Z         
2025-03-14T04:55:43.8268818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8270407Z         x_207: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8270487Z         
2025-03-14T04:55:43.8270785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8270933Z         out_129: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:55:43.8271003Z         
2025-03-14T04:55:43.8271288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8271746Z         x_208: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:55:43.8271841Z         
2025-03-14T04:55:43.8272120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:55:43.8273674Z         x_209: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:55:43.8273765Z         
2025-03-14T04:55:43.8274083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:55:43.8274251Z         x_209 += out_127;  out_130: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:55:43.8274318Z         
2025-03-14T04:55:43.8274609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:55:43.8274761Z         out_131: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:55:43.8274833Z         
2025-03-14T04:55:43.8275275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.8275439Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.8275519Z         
2025-03-14T04:55:43.8275822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.8275965Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:55:43.8276039Z         
2025-03-14T04:55:43.8276465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:55:43.8276625Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:55:43.8276692Z         
2025-03-14T04:55:43.8276991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.8277131Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:55:43.8277207Z         
2025-03-14T04:55:43.8277586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:55:43.8277776Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:55:43.8277892Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:55:43.8278023Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:55:43.8278090Z         
2025-03-14T04:55:43.8278422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:55:43.8278566Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:55:43.8278640Z         
2025-03-14T04:55:43.8278967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:55:43.8279096Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:55:43.8279162Z         
2025-03-14T04:55:43.8279571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:55:43.8279794Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:55:43.8279862Z         
2025-03-14T04:55:43.8280286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:55:43.8280415Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:55:43.8280849Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:55:43.8280973Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:55:43.8281101Z         x_210: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:55:43.8281167Z         
2025-03-14T04:55:43.8281640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.8281780Z         tensor: "f32[82125, 4][4, 1]cpu" = x_210.to(torch.float32);  x_210 = None
2025-03-14T04:55:43.8281855Z         
2025-03-14T04:55:43.8282155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:55:43.8282944Z         x_211: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_131, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_131 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:55:43.8283013Z         
2025-03-14T04:55:43.8283292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:55:43.8283483Z         x_212: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_211, inplace = False);  x_211 = None
2025-03-14T04:55:43.8283558Z         
2025-03-14T04:55:43.8283938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:55:43.8284822Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:55:43.8284902Z         
2025-03-14T04:55:43.8285293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:55:43.8286113Z         x_213: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_212, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_212 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:55:43.8286218Z         
2025-03-14T04:55:43.8286571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:55:43.8286724Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:55:43.8286873Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:55:43.8286938Z         
2025-03-14T04:55:43.8287362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:55:43.8287528Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_213.view(4, -1, 4, 73, 75);  x_213 = None
2025-03-14T04:55:43.8287704Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:55:43.8287887Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:55:43.8287954Z         
2025-03-14T04:55:43.8288356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:55:43.8288583Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:55:43.8288657Z         
2025-03-14T04:55:43.8289087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:55:43.8289245Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:55:43.8289394Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:55:43.8289543Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:55:43.8289609Z         
2025-03-14T04:55:43.8289993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:55:43.8290165Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:55:43.8290241Z         
2025-03-14T04:55:43.8290553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:55:43.8290703Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:55:43.8290769Z         
2025-03-14T04:55:43.8291105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:55:43.8291236Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:55:43.8291386Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.8291532Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:55:43.8291606Z         
2025-03-14T04:55:43.8291923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:55:43.8292053Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:55:43.8292217Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:55:43.8292371Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:55:43.8292436Z         
2025-03-14T04:55:43.8292748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:55:43.8292869Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:55:43.8292966Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:55:43.8293090Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:55:43.8293163Z         
2025-03-14T04:55:43.8293469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:55:43.8293623Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:55:43.8293715Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:55:43.8293853Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:55:43.8293920Z         
2025-03-14T04:55:43.8294271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:55:43.8294429Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:55:43.8294568Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:55:43.8294636Z         
2025-03-14T04:55:43.8294939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:55:43.8295097Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:55:43.8295211Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:55:43.8295284Z         
2025-03-14T04:55:43.8295579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:55:43.8295736Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:55:43.8295848Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:55:43.8295919Z         
2025-03-14T04:55:43.8296218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:55:43.8296404Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:55:43.8296517Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:55:43.8296591Z         
2025-03-14T04:55:43.8296943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:55:43.8297092Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:55:43.8297175Z         
2025-03-14T04:55:43.8297520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:55:43.8297657Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:55:43.8297730Z         
2025-03-14T04:55:43.8298072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:55:43.8298242Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:55:43.8298371Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:55:43.8298531Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:55:43.8298670Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:55:43.8298746Z         
2025-03-14T04:55:43.8299092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:55:43.8299241Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:55:43.8299362Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:55:43.8299523Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:55:43.8299660Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:55:43.8299734Z         
2025-03-14T04:55:43.8300061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:55:43.8300190Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:55:43.8300361Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:55:43.8300503Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:55:43.8300567Z         
2025-03-14T04:55:43.8300907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:55:43.8301031Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:55:43.8301197Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:55:43.8301338Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:55:43.8301404Z         
2025-03-14T04:55:43.8301724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:55:43.8301823Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:55:43.8301948Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:55:43.8302014Z         
2025-03-14T04:55:43.8302334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:55:43.8302432Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:55:43.8302570Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:55:43.8302636Z         
2025-03-14T04:55:43.8302942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:55:43.8303072Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:55:43.8303207Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:55:43.8303275Z         
2025-03-14T04:55:43.8303585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:55:43.8303697Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:55:43.8303850Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:55:43.8303920Z         
2025-03-14T04:55:43.8304359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:55:43.8304561Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:55:43.8304638Z         
2025-03-14T04:55:43.8304983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:55:43.8305155Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:55:43.8305222Z         
2025-03-14T04:55:43.8305620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:55:43.8305808Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:55:43.8305883Z         
2025-03-14T04:55:43.8306357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:55:43.8306501Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:55:43.8306586Z         
2025-03-14T04:55:43.8306890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.8307033Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:55:43.8307106Z         
2025-03-14T04:55:43.8307534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:55:43.8307655Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:55:43.8307760Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:55:43.8307881Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:55:43.8307947Z         
2025-03-14T04:55:43.8308411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:55:43.8308573Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:55:43.8308814Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:55:43.8308903Z         
2025-03-14T04:55:43.8309360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:55:43.8309548Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:55:43.8309614Z         
2025-03-14T04:55:43.8309917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:55:43.8310068Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:55:43.8310141Z         
2025-03-14T04:55:43.8310521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:55:43.8310698Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:55:43.8310763Z         
2025-03-14T04:55:43.8311074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:55:43.8311220Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:55:43.8311293Z         
2025-03-14T04:55:43.8311667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:55:43.8311819Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:55:43.8311888Z         
2025-03-14T04:55:43.8312375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:55:43.8312513Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:55:43.8312646Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:55:43.8312805Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:55:43.8312964Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:55:43.8313032Z         
2025-03-14T04:55:43.8313402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:55:43.8313521Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:55:43.8313596Z         
2025-03-14T04:56:03.5598343Z 
2025-03-14T04:56:03.5599241Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:03.5600690Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:56:03.5602300Z         l_features_res5_ = L_features_res5_
2025-03-14T04:56:03.5603172Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:56:03.5603737Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:56:03.5604224Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:56:03.5604833Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:56:03.5605418Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:56:03.5605984Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:56:03.5606693Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:56:03.5607080Z         
2025-03-14T04:56:03.5607745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:56:03.5608530Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:56:03.5608817Z         
2025-03-14T04:56:03.5609235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5609742Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:56:03.5610008Z         
2025-03-14T04:56:03.5610664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:56:03.5611419Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:56:03.5611695Z         
2025-03-14T04:56:03.5612085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5612593Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:56:03.5612861Z         
2025-03-14T04:56:03.5613394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:56:03.5614026Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:56:03.5614370Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:56:03.5614722Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:56:03.5614972Z         
2025-03-14T04:56:03.5615501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:56:03.5616032Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:56:03.5616288Z         
2025-03-14T04:56:03.5616719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:56:03.5617275Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:56:03.5617525Z         
2025-03-14T04:56:03.5618007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:56:03.5618685Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:56:03.5619026Z         
2025-03-14T04:56:03.5619545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:56:03.5620176Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:56:03.5620691Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:56:03.5621191Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:56:03.5621514Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:56:03.5621778Z         
2025-03-14T04:56:03.5622192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:56:03.5622692Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:56:03.5622950Z         
2025-03-14T04:56:03.5623311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:56:03.5624410Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:56:03.5625181Z         
2025-03-14T04:56:03.5625559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:56:03.5626083Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:03.5626396Z         
2025-03-14T04:56:03.5626878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:56:03.5627992Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:56:03.5628752Z         
2025-03-14T04:56:03.5629220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:56:03.5630224Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:56:03.5630930Z         
2025-03-14T04:56:03.5631353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:56:03.5631896Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:56:03.5632244Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:56:03.5632519Z         
2025-03-14T04:56:03.5633023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:56:03.5633657Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:56:03.5634038Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:56:03.5634434Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:56:03.5634727Z         
2025-03-14T04:56:03.5635221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:56:03.5635894Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:56:03.5636215Z         
2025-03-14T04:56:03.5636729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:56:03.5637363Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:56:03.5637718Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:56:03.5638060Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:56:03.5638318Z         
2025-03-14T04:56:03.5638780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:03.5639379Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:56:03.5639671Z         
2025-03-14T04:56:03.5640074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:03.5640581Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:56:03.5640840Z         
2025-03-14T04:56:03.5641257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:03.5641758Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:03.5642069Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:03.5642398Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:56:03.5642667Z         
2025-03-14T04:56:03.5643077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:03.5643565Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:03.5643859Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:03.5644178Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:03.5644439Z         
2025-03-14T04:56:03.5644831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:03.5645309Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:03.5645570Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:56:03.5645840Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:56:03.5646083Z         
2025-03-14T04:56:03.5646477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:03.5647006Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:03.5647299Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:56:03.5647586Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:56:03.5647826Z         
2025-03-14T04:56:03.5648231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:03.5648773Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:03.5649140Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:56:03.5649452Z         
2025-03-14T04:56:03.5649837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:03.5650338Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:03.5650658Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:56:03.5650893Z         
2025-03-14T04:56:03.5651275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:03.5651772Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:03.5652096Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:56:03.5652333Z         
2025-03-14T04:56:03.5652723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:03.5653342Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:03.5653696Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:56:03.5653962Z         
2025-03-14T04:56:03.5654460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:03.5655009Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:03.5655270Z         
2025-03-14T04:56:03.5655695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:03.5656233Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:03.5656501Z         
2025-03-14T04:56:03.5657090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:03.5657781Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:03.5658113Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:56:03.5658524Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:03.5658910Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:56:03.5659182Z         
2025-03-14T04:56:03.5659660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:03.5660219Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:03.5660546Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:56:03.5660916Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:03.5661280Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:56:03.5661555Z         
2025-03-14T04:56:03.5662000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:03.5662531Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:03.5662870Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:03.5663225Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:56:03.5663486Z         
2025-03-14T04:56:03.5663927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:03.5664540Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:03.5664900Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:03.5665275Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:56:03.5665545Z         
2025-03-14T04:56:03.5665962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:03.5666466Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:03.5666753Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:03.5666998Z         
2025-03-14T04:56:03.5667404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:03.5667885Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:03.5668181Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:03.5668423Z         
2025-03-14T04:56:03.5668827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:03.5669318Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:03.5669624Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:03.5669882Z         
2025-03-14T04:56:03.5670289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:03.5670779Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:03.5671079Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:03.5671339Z         
2025-03-14T04:56:03.5671794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:03.5672392Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:03.5672695Z         
2025-03-14T04:56:03.5673146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:03.5673709Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:56:03.5674020Z         
2025-03-14T04:56:03.5674505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:56:03.5675840Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:56:03.5676142Z         
2025-03-14T04:56:03.5676729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:56:03.5677461Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:56:03.5677726Z         
2025-03-14T04:56:03.5678123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5678626Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:56:03.5678899Z         
2025-03-14T04:56:03.5679434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:56:03.5680043Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:56:03.5680322Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:56:03.5680600Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:56:03.5680841Z         
2025-03-14T04:56:03.5681689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:56:03.5682399Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:56:03.5682868Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:56:03.5683291Z         
2025-03-14T04:56:03.5683850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:56:03.5684533Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:56:03.5684825Z         
2025-03-14T04:56:03.5685217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5685735Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:56:03.5686015Z         
2025-03-14T04:56:03.5686503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:56:03.5687079Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:56:03.5687344Z         
2025-03-14T04:56:03.5687727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:56:03.5688216Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:56:03.5688472Z         
2025-03-14T04:56:03.5688968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:56:03.5689541Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:56:03.5689834Z         
2025-03-14T04:56:03.5690406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:56:03.5691080Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:56:03.5691397Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:03.5691755Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:56:03.5692107Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:56:03.5693348Z         
2025-03-14T04:56:03.5693822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:56:03.5694374Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:56:03.5694616Z         
2025-03-14T04:56:03.5694797Z 
2025-03-14T04:56:03.5694902Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:03.5696209Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T04:56:03.5697493Z         l_features_res5_ = L_features_res5_
2025-03-14T04:56:03.5697932Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:56:03.5698449Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:56:03.5698925Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:56:03.5699449Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:56:03.5700024Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:56:03.5700581Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:56:03.5701120Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:56:03.5701480Z         
2025-03-14T04:56:03.5702004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:56:03.5702628Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T04:56:03.5702894Z         
2025-03-14T04:56:03.5703297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5703782Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:56:03.5704044Z         
2025-03-14T04:56:03.5704710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:56:03.5705372Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T04:56:03.5705639Z         
2025-03-14T04:56:03.5706028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5706550Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:56:03.5706815Z         
2025-03-14T04:56:03.5707289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:56:03.5707913Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:56:03.5708256Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T04:56:03.5708533Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:56:03.5708781Z         
2025-03-14T04:56:03.5709262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:56:03.5709845Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:56:03.5710104Z         
2025-03-14T04:56:03.5710571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:56:03.5711125Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:56:03.5711376Z         
2025-03-14T04:56:03.5711899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:56:03.5712622Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:56:03.5712966Z         
2025-03-14T04:56:03.5713544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:56:03.5714211Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:56:03.5714762Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:56:03.5715304Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:56:03.5715616Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:56:03.5715846Z         
2025-03-14T04:56:03.5716233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:56:03.5716710Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:56:03.5716977Z         
2025-03-14T04:56:03.5717326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:56:03.5718253Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:56:03.5718977Z         
2025-03-14T04:56:03.5719348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:56:03.5719869Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:03.5720172Z         
2025-03-14T04:56:03.5720645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:56:03.5721733Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:56:03.5722481Z         
2025-03-14T04:56:03.5722933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:56:03.5723943Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:56:03.5724655Z         
2025-03-14T04:56:03.5725083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:56:03.5725626Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:56:03.5725969Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:56:03.5726250Z         
2025-03-14T04:56:03.5726753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:56:03.5727371Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T04:56:03.5727752Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T04:56:03.5728150Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T04:56:03.5728443Z         
2025-03-14T04:56:03.5728933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:56:03.5729599Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:56:03.5729914Z         
2025-03-14T04:56:03.5730418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:56:03.5731068Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:56:03.5731422Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:56:03.5731766Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:56:03.5732048Z         
2025-03-14T04:56:03.5732514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:03.5733103Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:56:03.5733395Z         
2025-03-14T04:56:03.5733791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:03.5734328Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:56:03.5734588Z         
2025-03-14T04:56:03.5734981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:03.5735469Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:03.5735773Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:03.5736093Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:56:03.5736354Z         
2025-03-14T04:56:03.5736743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:03.5737225Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:03.5737521Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:03.5737842Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:03.5738110Z         
2025-03-14T04:56:03.5738520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:03.5738998Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:03.5739262Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:56:03.5739541Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T04:56:03.5739781Z         
2025-03-14T04:56:03.5740177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:03.5740873Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:03.5741168Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:56:03.5741445Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T04:56:03.5741697Z         
2025-03-14T04:56:03.5742113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:03.5742625Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:03.5742950Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:56:03.5743184Z         
2025-03-14T04:56:03.5743577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:03.5744082Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:03.5744510Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:56:03.5744752Z         
2025-03-14T04:56:03.5745156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:03.5745671Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:03.5746029Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T04:56:03.5746264Z         
2025-03-14T04:56:03.5746656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:03.5747192Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:03.5747570Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T04:56:03.5747812Z         
2025-03-14T04:56:03.5748250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:03.5748799Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:03.5749068Z         
2025-03-14T04:56:03.5749500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:03.5750036Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:03.5750299Z         
2025-03-14T04:56:03.5750736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:03.5751301Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:03.5751639Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T04:56:03.5751989Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:03.5752356Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T04:56:03.5752628Z         
2025-03-14T04:56:03.5753104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:03.5753674Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:03.5754009Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T04:56:03.5754355Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:03.5754719Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T04:56:03.5754994Z         
2025-03-14T04:56:03.5755427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:03.5755953Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:03.5756298Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:03.5756663Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T04:56:03.5756931Z         
2025-03-14T04:56:03.5757372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:03.5757948Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:03.5758288Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:03.5758644Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T04:56:03.5758919Z         
2025-03-14T04:56:03.5759319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:03.5759787Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:03.5760055Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:03.5760294Z         
2025-03-14T04:56:03.5760686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:03.5761169Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:03.5761436Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:03.5761670Z         
2025-03-14T04:56:03.5762064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:03.5762541Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:03.5762835Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:03.5763094Z         
2025-03-14T04:56:03.5763485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:03.5763964Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:03.5764259Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:03.5764509Z         
2025-03-14T04:56:03.5764943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:03.5765526Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:03.5765826Z         
2025-03-14T04:56:03.5766274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:03.5766824Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:56:03.5767108Z         
2025-03-14T04:56:03.5767576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:56:03.5768181Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:56:03.5768472Z         
2025-03-14T04:56:03.5769054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:56:03.5769732Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:56:03.5769980Z         
2025-03-14T04:56:03.5770355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5770836Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:56:03.5771092Z         
2025-03-14T04:56:03.5771615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:56:03.5772197Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T04:56:03.5772466Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:56:03.5772752Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:56:03.5772973Z         
2025-03-14T04:56:03.5773510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:56:03.5774162Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:56:03.5774629Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T04:56:03.5774973Z         
2025-03-14T04:56:03.5775504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:56:03.5776159Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:56:03.5776437Z         
2025-03-14T04:56:03.5776809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:03.5777313Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:56:03.5777591Z         
2025-03-14T04:56:03.5778059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:56:03.5778643Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:56:03.5778912Z         
2025-03-14T04:56:03.5779297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:56:03.5779793Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T04:56:03.5780061Z         
2025-03-14T04:56:03.5780546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:56:03.5781116Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:56:03.5781385Z         
2025-03-14T04:56:03.5782185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:56:03.5782939Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T04:56:03.5783289Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:03.5783659Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:56:03.5784043Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:56:03.5784374Z         
2025-03-14T04:56:03.5784847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:56:03.5785413Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:56:03.5785652Z         
2025-03-14T04:56:07.0294322Z 
2025-03-14T04:56:07.0300528Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:07.0301403Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T04:56:07.0301983Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:56:07.0302273Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:56:07.0302594Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:56:07.0302908Z         
2025-03-14T04:56:07.0303540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:56:07.0304421Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:56:07.0304898Z         
2025-03-14T04:56:07.0305537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:56:07.0306237Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:56:07.0306649Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:56:07.0307053Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:56:07.0307322Z         
2025-03-14T04:56:07.0307802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:07.0308417Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:56:07.0308758Z         
2025-03-14T04:56:07.0309167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:07.0309691Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:56:07.0309961Z         
2025-03-14T04:56:07.0310419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:07.0310936Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:07.0311252Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:07.0311579Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:56:07.0311852Z         
2025-03-14T04:56:07.0312275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:07.0312792Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:07.0313114Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:07.0313445Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:56:07.0313717Z         
2025-03-14T04:56:07.0314118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:07.0314609Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:07.0314876Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T04:56:07.0315144Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:56:07.0315391Z         
2025-03-14T04:56:07.0315818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:07.0316341Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:07.0316668Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T04:56:07.0316944Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:56:07.0317195Z         
2025-03-14T04:56:07.0317619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:07.0318134Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:07.0318483Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:56:07.0318722Z         
2025-03-14T04:56:07.0319110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:07.0319611Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:07.0319935Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:56:07.0320170Z         
2025-03-14T04:56:07.0320557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:07.0321056Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:07.0321377Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:56:07.0321616Z         
2025-03-14T04:56:07.0322006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:07.0322545Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:07.0322894Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:56:07.0323131Z         
2025-03-14T04:56:07.0323573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:07.0324104Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:07.0324366Z         
2025-03-14T04:56:07.0324786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:07.0325308Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:07.0325564Z         
2025-03-14T04:56:07.0325996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:07.0326533Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:07.0326859Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:56:07.0327196Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:07.0327545Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:56:07.0327803Z         
2025-03-14T04:56:07.0328234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:07.0328799Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:07.0329113Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:56:07.0329438Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:07.0329807Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:56:07.0330067Z         
2025-03-14T04:56:07.0330491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:07.0330993Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:07.0331328Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:07.0331703Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:56:07.0331960Z         
2025-03-14T04:56:07.0332382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:07.0332890Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:07.0333229Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:07.0333587Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:56:07.0333847Z         
2025-03-14T04:56:07.0334248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:07.0334720Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:07.0334994Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:07.0335233Z         
2025-03-14T04:56:07.0335630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:07.0336090Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:07.0336359Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:07.0336597Z         
2025-03-14T04:56:07.0337010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:07.0337490Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:07.0337789Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:07.0338046Z         
2025-03-14T04:56:07.0338438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:07.0338915Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:07.0339206Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:07.0339456Z         
2025-03-14T04:56:07.0339888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:07.0340470Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:07.0340764Z         
2025-03-14T04:56:07.0341186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:07.0341760Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T04:56:07.0342050Z         
2025-03-14T04:56:07.0342514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:56:07.0343144Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:56:07.0343449Z         
2025-03-14T04:56:07.0344023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:56:07.0344941Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:56:07.0345263Z         
2025-03-14T04:56:07.0345668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:07.0346161Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:56:07.0346454Z         
2025-03-14T04:56:07.0346981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:56:07.0347648Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:56:07.0347986Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T04:56:07.0348260Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:56:07.0348493Z         
2025-03-14T04:56:07.0349050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:56:07.0349731Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:56:07.0350188Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T04:56:07.0350543Z         
2025-03-14T04:56:07.0351110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:56:07.0351792Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:56:07.0352081Z         
2025-03-14T04:56:07.0352469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:56:07.0352980Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T04:56:07.0353253Z         
2025-03-14T04:56:07.0353726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:56:07.0354320Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T04:56:07.0354591Z         
2025-03-14T04:56:07.0354978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:56:07.0355475Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T04:56:07.0355744Z         
2025-03-14T04:56:07.0356232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:56:07.0356806Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T04:56:07.0357072Z         
2025-03-14T04:56:07.0357641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:56:07.0358331Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:56:07.0358646Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:07.0358981Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:56:07.0359347Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:56:07.0359616Z         
2025-03-14T04:56:07.0360070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:56:07.0360600Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:56:07.0360838Z         
2025-03-14T04:56:14.1727331Z 
2025-03-14T04:56:14.1729737Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:14.1732133Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:14.1734752Z         l_stack0_ = L_stack0_
2025-03-14T04:56:14.1735154Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:56:14.1735648Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:56:14.1736124Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:56:14.1736640Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:56:14.1737249Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:56:14.1737846Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:56:14.1738414Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:56:14.1738979Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:56:14.1739483Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1739987Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1740409Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1740823Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1741202Z         
2025-03-14T04:56:14.1741651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:56:14.1742145Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:56:14.1742853Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:56:14.1743625Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:14.1744534Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:56:14.1745349Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:56:14.1745672Z         
2025-03-14T04:56:14.1746083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:56:14.1747055Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:56:14.1747759Z         
2025-03-14T04:56:14.1748174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:56:14.1749191Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:56:14.1749923Z         
2025-03-14T04:56:14.1752005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1752564Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1752834Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:14.1753076Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:14.1753358Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1753625Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:14.1753879Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:14.1754105Z         
2025-03-14T04:56:14.1754516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:14.1755461Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1756231Z         
2025-03-14T04:56:14.1756696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:14.1757271Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:56:14.1757576Z         
2025-03-14T04:56:14.1757986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:14.1758517Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:14.1758796Z         
2025-03-14T04:56:14.1759201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:14.1759728Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:14.1760042Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1760366Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:14.1760640Z         
2025-03-14T04:56:14.1761038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:14.1762938Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:14.1763619Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:14.1763971Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:14.1764252Z         
2025-03-14T04:56:14.1764670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:14.1765164Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1765425Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:14.1765686Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:14.1765932Z         
2025-03-14T04:56:14.1766372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:14.1766881Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:14.1767169Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:14.1767434Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:14.1767681Z         
2025-03-14T04:56:14.1768110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:14.1768620Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:14.1768950Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:14.1769187Z         
2025-03-14T04:56:14.1769573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:14.1770105Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:14.1770428Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:14.1770661Z         
2025-03-14T04:56:14.1771043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:14.1771568Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:14.1771891Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:14.1772127Z         
2025-03-14T04:56:14.1772516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:14.1773143Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:14.1773499Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:14.1773738Z         
2025-03-14T04:56:14.1774166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:14.1774725Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:14.1774984Z         
2025-03-14T04:56:14.1775401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:14.1775927Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:14.1776184Z         
2025-03-14T04:56:14.1776619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:14.1777157Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:14.1777475Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:14.1777810Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:14.1778160Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:14.1778423Z         
2025-03-14T04:56:14.1778880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:14.1779416Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:14.1779752Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:14.1780084Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:14.1780429Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:14.1780687Z         
2025-03-14T04:56:14.1781125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:14.1782455Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:14.1782924Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:14.1783299Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:14.1783571Z         
2025-03-14T04:56:14.1784035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:14.1784655Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:14.1785002Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:14.1785371Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:14.1785724Z         
2025-03-14T04:56:14.1786152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:14.1786665Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:14.1786981Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:14.1787219Z         
2025-03-14T04:56:14.1787669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:14.1788130Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:14.1788390Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:14.1788625Z         
2025-03-14T04:56:14.1789066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:14.1789545Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:14.1789841Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:14.1790094Z         
2025-03-14T04:56:14.1790491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:14.1790971Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:14.1791261Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:14.1791509Z         
2025-03-14T04:56:14.1791949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:14.1792544Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:14.1792915Z         
2025-03-14T04:56:14.1793350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:14.1793914Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:14.1794202Z         
2025-03-14T04:56:14.1794681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:14.1795296Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:14.1795666Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:14.1795963Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:14.1796273Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:14.1796599Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:14.1796863Z         
2025-03-14T04:56:14.1797242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1797803Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1798154Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:14.1798403Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:14.1798773Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1799132Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:14.1799408Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:14.1799650Z         
2025-03-14T04:56:14.1800077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:14.1800659Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:56:14.1800954Z         
2025-03-14T04:56:14.1801405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:14.1802017Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:14.1802401Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:14.1802722Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:14.1803037Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:14.1803368Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:14.1803635Z         
2025-03-14T04:56:14.1804207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:14.1804920Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:14.1805271Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:14.1805621Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:14.1805973Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:14.1806277Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:14.1806528Z         
2025-03-14T04:56:14.1806983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:14.1807519Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:14.1807763Z         
2025-03-14T04:56:14.1807904Z 
2025-03-14T04:56:14.1808017Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:14.1809950Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:14.1812031Z         l_stack0_ = L_stack0_
2025-03-14T04:56:14.1812392Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:56:14.1812878Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:56:14.1813356Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:56:14.1813818Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:56:14.1814350Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:56:14.1814911Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:56:14.1815469Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:56:14.1816021Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:56:14.1816517Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1816921Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1817324Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1817726Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1818030Z         
2025-03-14T04:56:14.1818412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:56:14.1818900Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:56:14.1819611Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:56:14.1820330Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:14.1821072Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:56:14.1821776Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:56:14.1822058Z         
2025-03-14T04:56:14.1822459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:56:14.1823416Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:56:14.1824214Z         
2025-03-14T04:56:14.1824682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:56:14.1825759Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:56:14.1826535Z         
2025-03-14T04:56:14.1826967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1827436Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1827695Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:14.1827933Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:14.1828234Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1828498Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:14.1828750Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:14.1828974Z         
2025-03-14T04:56:14.1829345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:14.1830282Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1830997Z         
2025-03-14T04:56:14.1831457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:14.1832030Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:56:14.1832302Z         
2025-03-14T04:56:14.1832695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:14.1833213Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:14.1833496Z         
2025-03-14T04:56:14.1833899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:14.1834396Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:14.1834702Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1835021Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:14.1835289Z         
2025-03-14T04:56:14.1835709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:14.1836206Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:14.1836500Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:14.1836821Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:14.1837082Z         
2025-03-14T04:56:14.1837483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:14.1837965Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1838226Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:14.1838484Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:14.1838713Z         
2025-03-14T04:56:14.1839115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:14.1839620Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:14.1839908Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:14.1840173Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:14.1840449Z         
2025-03-14T04:56:14.1840870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:14.1841386Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:14.1841731Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:14.1841966Z         
2025-03-14T04:56:14.1842352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:14.1842862Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:14.1843182Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:14.1843437Z         
2025-03-14T04:56:14.1843832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:14.1844344Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:14.1844666Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:14.1844905Z         
2025-03-14T04:56:14.1845300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:14.1845850Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:14.1846198Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:14.1846436Z         
2025-03-14T04:56:14.1846869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:14.1847413Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:14.1847673Z         
2025-03-14T04:56:14.1848095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:14.1848641Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:14.1848896Z         
2025-03-14T04:56:14.1849326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:14.1849865Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:14.1850185Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:14.1850519Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:14.1850863Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:14.1851121Z         
2025-03-14T04:56:14.1851560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:14.1852098Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:14.1852412Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:14.1852740Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:14.1853081Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:14.1853340Z         
2025-03-14T04:56:14.1853782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:14.1854285Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:14.1854628Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:14.1854969Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:14.1855217Z         
2025-03-14T04:56:14.1855632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:14.1856130Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:14.1856473Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:14.1856819Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:14.1857068Z         
2025-03-14T04:56:14.1857458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:14.1857942Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:14.1858212Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:14.1858469Z         
2025-03-14T04:56:14.1858889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:14.1859373Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:14.1859652Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:14.1859901Z         
2025-03-14T04:56:14.1860317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:14.1860816Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:14.1861128Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:14.1861389Z         
2025-03-14T04:56:14.1861830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:14.1862334Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:14.1862640Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:14.1862899Z         
2025-03-14T04:56:14.1863415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:14.1864072Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:14.1865107Z         
2025-03-14T04:56:14.1865621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:14.1866244Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:14.1866555Z         
2025-03-14T04:56:14.1867023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:14.1867658Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:14.1868075Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:14.1868377Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:14.1868690Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:14.1869021Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:14.1869312Z         
2025-03-14T04:56:14.1869705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1870281Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1870643Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:14.1870893Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:14.1871295Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1871661Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:14.1871917Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:14.1872149Z         
2025-03-14T04:56:14.1872599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:14.1873180Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:56:14.1873481Z         
2025-03-14T04:56:14.1873929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:14.1874559Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:14.1874929Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:14.1875266Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:14.1875567Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:14.1875886Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:14.1876152Z         
2025-03-14T04:56:14.1876717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:14.1877418Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:14.1877768Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:14.1878114Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:14.1878458Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:14.1878756Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:14.1879000Z         
2025-03-14T04:56:14.1879451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:14.1880012Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:14.1880246Z         
2025-03-14T04:56:14.1880387Z 
2025-03-14T04:56:14.1880485Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:14.1882614Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:14.1884609Z         l_stack0_ = L_stack0_
2025-03-14T04:56:14.1884982Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:56:14.1885458Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:56:14.1885925Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:56:14.1886393Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:56:14.1886904Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:56:14.1887465Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:56:14.1888022Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:56:14.1888586Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:56:14.1889063Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1889468Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1889864Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1890285Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1890580Z         
2025-03-14T04:56:14.1890955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:56:14.1891436Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:56:14.1892119Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:56:14.1892828Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:14.1893545Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:56:14.1894255Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:56:14.1894538Z         
2025-03-14T04:56:14.1894962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:56:14.1895919Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:56:14.1896634Z         
2025-03-14T04:56:14.1897048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:56:14.1898039Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:56:14.1898795Z         
2025-03-14T04:56:14.1899174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1899641Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1899896Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:14.1900131Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:14.1900410Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1900674Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:14.1900923Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:14.1901147Z         
2025-03-14T04:56:14.1901523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:14.1902513Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1903263Z         
2025-03-14T04:56:14.1903756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:14.1904508Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:56:14.1904803Z         
2025-03-14T04:56:14.1905232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:14.1905786Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:14.1906073Z         
2025-03-14T04:56:14.1906478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:14.1906982Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:14.1907288Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1907608Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:14.1907871Z         
2025-03-14T04:56:14.1908302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:14.1908827Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:14.1909139Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:14.1909491Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:14.1909777Z         
2025-03-14T04:56:14.1910181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:14.1910687Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1910946Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:14.1911206Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:14.1911451Z         
2025-03-14T04:56:14.1911849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:14.1912375Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:14.1912661Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:14.1912923Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:14.1913165Z         
2025-03-14T04:56:14.1913572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:14.1914083Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:14.1914410Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:14.1914650Z         
2025-03-14T04:56:14.1915036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:14.1915541Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:14.1915864Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:14.1916099Z         
2025-03-14T04:56:14.1916486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:14.1916987Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:14.1917311Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:14.1917537Z         
2025-03-14T04:56:14.1917945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:14.1918485Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:14.1918860Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:14.1919099Z         
2025-03-14T04:56:14.1919527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:14.1920062Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:14.1920327Z         
2025-03-14T04:56:14.1920756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:14.1921283Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:14.1921537Z         
2025-03-14T04:56:14.1921973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:14.1922532Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:14.1922857Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:14.1923192Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:14.1923556Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:14.1923815Z         
2025-03-14T04:56:14.1924257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:14.1924801Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:14.1925118Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:14.1925464Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:14.1925805Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:14.1926062Z         
2025-03-14T04:56:14.1926485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:14.1926991Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:14.1927313Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:14.1927645Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:14.1927891Z         
2025-03-14T04:56:14.1928304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:14.1928797Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:14.1929119Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:14.1929457Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:14.1929706Z         
2025-03-14T04:56:14.1930096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:14.1930565Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:14.1930826Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:14.1931059Z         
2025-03-14T04:56:14.1931456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:14.1931917Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:14.1932176Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:14.1932407Z         
2025-03-14T04:56:14.1932803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:14.1933280Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:14.1933570Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:14.1933811Z         
2025-03-14T04:56:14.1936653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:14.1937279Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:14.1937593Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:14.1937845Z         
2025-03-14T04:56:14.1938344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:14.1938933Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:14.1939291Z         
2025-03-14T04:56:14.1939720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:14.1940287Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:14.1940577Z         
2025-03-14T04:56:14.1941024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:14.1941676Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:14.1942042Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:14.1942334Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:14.1942646Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:14.1942966Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:14.1943230Z         
2025-03-14T04:56:14.1943616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1944268Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1944630Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:14.1944877Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:14.1945250Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1945616Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:14.1945908Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:14.1946152Z         
2025-03-14T04:56:14.1946609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:14.1947169Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:56:14.1947458Z         
2025-03-14T04:56:14.1947903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:14.1948509Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:14.1948868Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:14.1949163Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:14.1949469Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:14.1949787Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:14.1950050Z         
2025-03-14T04:56:14.1950600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:14.1951298Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:14.1951662Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:14.1952005Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:14.1952348Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:14.1952662Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:14.1952908Z         
2025-03-14T04:56:14.1953344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:14.1953863Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:14.1954092Z         
2025-03-14T04:56:14.1954248Z 
2025-03-14T04:56:14.1954340Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:14.1956270Z     def forward(self, L_stack0_: "f32[3225, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:14.1958308Z         l_stack0_ = L_stack0_
2025-03-14T04:56:14.1958653Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:56:14.1959131Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:56:14.1959609Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:56:14.1960088Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:56:14.1960600Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:56:14.1961155Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:56:14.1961709Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:56:14.1962262Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:56:14.1962739Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1964237Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1964753Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1965159Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:14.1965446Z         
2025-03-14T04:56:14.1965828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:56:14.1966355Z         x: "f32[3225, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:56:14.1967056Z         x_1: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:56:14.1967805Z         x_2: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:56:14.1968520Z         x_3: "f32[3225, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:56:14.1969246Z         x_4: "f32[3225, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:56:14.1969527Z         
2025-03-14T04:56:14.1969931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:56:14.1970892Z         scores: "f32[3225, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:56:14.1971599Z         
2025-03-14T04:56:14.1972015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:56:14.1973064Z         proposal_deltas: "f32[3225, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:56:14.1973801Z         
2025-03-14T04:56:14.1974183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.1977224Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1977498Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:14.1978192Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:14.1978478Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:14.1978748Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:14.1979000Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:14.1979231Z         
2025-03-14T04:56:14.1979612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:14.1980556Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.1981271Z         
2025-03-14T04:56:14.1981936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:14.1982518Z         deltas: "f32[3225, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:56:14.1982792Z         
2025-03-14T04:56:14.1983191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:14.1983800Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:14.1984178Z         
2025-03-14T04:56:14.1984641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:14.1985232Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:14.1985561Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1985927Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:14.1986199Z         
2025-03-14T04:56:14.1986608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:14.1987144Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:14.1987446Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:14.1987765Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:14.1988030Z         
2025-03-14T04:56:14.1988431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:14.1988920Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:14.1989180Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:14.1989438Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:14.1989678Z         
2025-03-14T04:56:14.1990078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:14.1990583Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:14.1990868Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:14.1991132Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:14.1991378Z         
2025-03-14T04:56:14.1991792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:14.1992334Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:14.1992664Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:14.1992903Z         
2025-03-14T04:56:14.1993286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:14.1993796Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:14.1994120Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:14.1994358Z         
2025-03-14T04:56:14.1994743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:14.1995249Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:14.1995579Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:14.1995817Z         
2025-03-14T04:56:14.1996202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:14.1996738Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:14.1997106Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:14.1997343Z         
2025-03-14T04:56:14.1997772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:14.1998340Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:14.1998599Z         
2025-03-14T04:56:14.1999015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:14.1999532Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:14.1999782Z         
2025-03-14T04:56:14.2000236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:14.2000769Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:14.2001084Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:14.2001416Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:14.2001762Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:14.2002016Z         
2025-03-14T04:56:14.2002451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:14.2002989Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:14.2003309Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:14.2003635Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:14.2003978Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:14.2004236Z         
2025-03-14T04:56:14.2004657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:14.2005186Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:14.2005510Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:14.2005856Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:14.2006104Z         
2025-03-14T04:56:14.2006529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:14.2007031Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:14.2007356Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:14.2007704Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:14.2007960Z         
2025-03-14T04:56:14.2008361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:14.2010058Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:14.2010349Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:14.2010590Z         
2025-03-14T04:56:14.2011075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:14.2011548Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:14.2011813Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:14.2012054Z         
2025-03-14T04:56:14.2012449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:14.2012956Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:14.2013253Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:14.2013502Z         
2025-03-14T04:56:14.2013896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:14.2014403Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:14.2014696Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:14.2014947Z         
2025-03-14T04:56:14.2015380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:14.2015966Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:14.2016261Z         
2025-03-14T04:56:14.2016689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:14.2017240Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:14.2017553Z         
2025-03-14T04:56:14.2017998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:14.2018611Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:14.2018974Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:14.2019268Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:14.2019593Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:14.2019963Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:14.2020234Z         
2025-03-14T04:56:14.2020616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:14.2021174Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.2021528Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:14.2021773Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:14.2022141Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:14.2022494Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:14.2022744Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:14.2022965Z         
2025-03-14T04:56:14.2023386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:14.2023938Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:56:14.2024304Z         
2025-03-14T04:56:14.2024816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:14.2025460Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:14.2025836Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:14.2026157Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:14.2026466Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:14.2026792Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:14.2027060Z         
2025-03-14T04:56:14.2027617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:14.2028349Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:14.2028708Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:14.2029065Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:14.2029420Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:14.2029730Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:14.2029985Z         
2025-03-14T04:56:14.2030442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:14.2030982Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:14.2031234Z         
2025-03-14T04:56:16.4149033Z 
2025-03-14T04:56:16.4153458Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:16.4154589Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:16.4155727Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:56:16.4157467Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:56:16.4157920Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4160892Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4161378Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4166483Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4167680Z         
2025-03-14T04:56:16.4168256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4168837Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4169110Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:16.4169355Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:16.4169665Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4169951Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:16.4170206Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:16.4170588Z         
2025-03-14T04:56:16.4171288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:16.4172829Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4173721Z         
2025-03-14T04:56:16.4174325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:16.4175035Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:56:16.4175406Z         
2025-03-14T04:56:16.4176022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:16.4176758Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:16.4177148Z         
2025-03-14T04:56:16.4177662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:16.4178296Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:16.4178716Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4179123Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:16.4179468Z         
2025-03-14T04:56:16.4180044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:16.4180669Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:16.4181116Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:16.4181797Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:16.4182159Z         
2025-03-14T04:56:16.4195241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:16.4195966Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4196386Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:16.4196673Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:16.4196933Z         
2025-03-14T04:56:16.4197377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:16.4197953Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:16.4198280Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:16.4198623Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:16.4198880Z         
2025-03-14T04:56:16.4199317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:16.4199859Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:16.4200208Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:16.4200462Z         
2025-03-14T04:56:16.4200874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:16.4201392Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:16.4201781Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:16.4202032Z         
2025-03-14T04:56:16.4202441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:16.4203013Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:16.4203345Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:16.4203585Z         
2025-03-14T04:56:16.4203976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:16.4204518Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:16.4204927Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:16.4205167Z         
2025-03-14T04:56:16.4205603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:16.4206153Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:16.4206426Z         
2025-03-14T04:56:16.4206856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:16.4207391Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:16.4207652Z         
2025-03-14T04:56:16.4208093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:16.4208643Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:16.4208964Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:16.4209301Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:16.4209652Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:16.4209921Z         
2025-03-14T04:56:16.4210377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:16.4210926Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:16.4211238Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:16.4211567Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:16.4211912Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:16.4212178Z         
2025-03-14T04:56:16.4212602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:16.4213110Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:16.4213439Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:16.4213785Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:16.4214044Z         
2025-03-14T04:56:16.4214477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:16.4215007Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:16.4215349Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:16.4215706Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:16.4215982Z         
2025-03-14T04:56:16.4216396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:16.4216872Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:16.4217141Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:16.4217382Z         
2025-03-14T04:56:16.4217785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:16.4218286Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:16.4218563Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:16.4218803Z         
2025-03-14T04:56:16.4219205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:16.4219690Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:16.4219994Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:16.4220253Z         
2025-03-14T04:56:16.4220649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:16.4221127Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:16.4221433Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:16.4221688Z         
2025-03-14T04:56:16.4222129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:16.4222713Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:16.4223015Z         
2025-03-14T04:56:16.4223457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:16.4224030Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:16.4224439Z         
2025-03-14T04:56:16.4224911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:16.4225543Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:16.4225916Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:16.4226214Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:16.4226533Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:16.4226863Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:16.4227135Z         
2025-03-14T04:56:16.4227530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4228101Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4228460Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:16.4228729Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:16.4229104Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4229473Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:16.4229749Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:16.4229981Z         
2025-03-14T04:56:16.4230420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:16.4231037Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:56:16.4231375Z         
2025-03-14T04:56:16.4231850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:16.4232467Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:16.4232840Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:16.4233143Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:16.4233448Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:16.4233773Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:16.4234040Z         
2025-03-14T04:56:16.4234602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:16.4235319Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:16.4235670Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:16.4236022Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:16.4236373Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:16.4236681Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:16.4236929Z         
2025-03-14T04:56:16.4237416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:16.4237955Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:16.4238202Z         
2025-03-14T04:56:16.4238299Z 
2025-03-14T04:56:16.4238406Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:16.4239262Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:16.4240064Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:56:16.4240301Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:56:16.4240627Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4241038Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4241443Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4241892Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4242186Z         
2025-03-14T04:56:16.4242567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4243037Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4243318Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:16.4243566Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:16.4243861Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4244137Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:16.4244396Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:16.4244633Z         
2025-03-14T04:56:16.4245031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:16.4246023Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4246760Z         
2025-03-14T04:56:16.4247230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:16.4247813Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:56:16.4248085Z         
2025-03-14T04:56:16.4248485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:16.4249015Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:16.4249298Z         
2025-03-14T04:56:16.4249700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:16.4250194Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:16.4250497Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4250838Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:16.4251107Z         
2025-03-14T04:56:16.4251519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:16.4252016Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:16.4252319Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:16.4252640Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:16.4252908Z         
2025-03-14T04:56:16.4253304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:16.4253792Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4254052Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:16.4254308Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:16.4254547Z         
2025-03-14T04:56:16.4254954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:16.4255464Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:16.4255770Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:16.4256036Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:16.4256280Z         
2025-03-14T04:56:16.4256682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:16.4257224Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:16.4257558Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:16.4257794Z         
2025-03-14T04:56:16.4258185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:16.4258716Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:16.4259046Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:16.4259286Z         
2025-03-14T04:56:16.4259674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:16.4260179Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:16.4260502Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:16.4260742Z         
2025-03-14T04:56:16.4261134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:16.4261670Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:16.4262016Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:16.4262250Z         
2025-03-14T04:56:16.4262675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:16.4263204Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:16.4263466Z         
2025-03-14T04:56:16.4263918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:16.4264581Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:16.4264861Z         
2025-03-14T04:56:16.4265350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:16.4265915Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:16.4266251Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:16.4266611Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:16.4266979Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:16.4267253Z         
2025-03-14T04:56:16.4267719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:16.4268300Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:16.4268639Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:16.4268990Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:16.4269384Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:16.4269652Z         
2025-03-14T04:56:16.4270101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:16.4270668Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:16.4271016Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:16.4271384Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:16.4271652Z         
2025-03-14T04:56:16.4272096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:16.4272647Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:16.4272997Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:16.4273362Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:16.4273634Z         
2025-03-14T04:56:16.4274058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:16.4274547Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:16.4274824Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:16.4275072Z         
2025-03-14T04:56:16.4275493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:16.4275983Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:16.4276251Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:16.4276490Z         
2025-03-14T04:56:16.4276882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:16.4277363Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:16.4277654Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:16.4277942Z         
2025-03-14T04:56:16.4278340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:16.4278821Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:16.4279109Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:16.4279355Z         
2025-03-14T04:56:16.4279799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:16.4280390Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:16.4280687Z         
2025-03-14T04:56:16.4281120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:16.4281908Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:16.4282201Z         
2025-03-14T04:56:16.4282659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:16.4283355Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:16.4283724Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:16.4284023Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:16.4284360Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:16.4284682Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:16.4284951Z         
2025-03-14T04:56:16.4285338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4285908Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4286289Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:16.4286537Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:16.4286904Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4287262Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:16.4287513Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:16.4287741Z         
2025-03-14T04:56:16.4288161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:16.4288758Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:56:16.4289090Z         
2025-03-14T04:56:16.4289533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:16.4290139Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:16.4290503Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:16.4290793Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:16.4291097Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:16.4291442Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:16.4291706Z         
2025-03-14T04:56:16.4292255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:16.4292939Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:16.4293276Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:16.4293620Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:16.4293957Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:16.4294254Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:16.4294493Z         
2025-03-14T04:56:16.4294932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:16.4295459Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:16.4295693Z         
2025-03-14T04:56:16.4295840Z 
2025-03-14T04:56:16.4295934Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:16.4296746Z     def forward(self, L_predictions_0_: "f32[3225, 81][81, 1]cpu", L_predictions_1_: "f32[3225, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1225 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1225 - s0, 4][4, 1]cpu"):
2025-03-14T04:56:16.4297538Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:56:16.4297774Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:56:16.4298088Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4298490Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4298889Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4299302Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:56:16.4299600Z         
2025-03-14T04:56:16.4299988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4300457Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4300712Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:56:16.4300948Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:56:16.4301224Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:56:16.4301486Z         getitem_2: "Sym(1225 - s0)" = size_1[0]
2025-03-14T04:56:16.4301741Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:56:16.4301967Z         
2025-03-14T04:56:16.4302340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:56:16.4303288Z         proposal_boxes: "f32[3225, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4304015Z         
2025-03-14T04:56:16.4304542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:56:16.4305149Z         deltas: "f32[3225, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:56:16.4305424Z         
2025-03-14T04:56:16.4305829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:56:16.4306360Z         boxes: "f32[3225, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:56:16.4306646Z         
2025-03-14T04:56:16.4307063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:56:16.4307567Z         getitem_4: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:56:16.4307873Z         getitem_5: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4308194Z         widths: "f32[3225][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T04:56:16.4308463Z         
2025-03-14T04:56:16.4308868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:56:16.4309363Z         getitem_6: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:56:16.4309667Z         getitem_7: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:56:16.4310000Z         heights: "f32[3225][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T04:56:16.4310267Z         
2025-03-14T04:56:16.4310668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:56:16.4311177Z         getitem_8: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:56:16.4311440Z         mul: "f32[3225][1]cpu" = 0.5 * widths
2025-03-14T04:56:16.4311698Z         ctr_x: "f32[3225][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T04:56:16.4311940Z         
2025-03-14T04:56:16.4312341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:56:16.4312851Z         getitem_9: "f32[3225][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:56:16.4313157Z         mul_1: "f32[3225][1]cpu" = 0.5 * heights
2025-03-14T04:56:16.4313419Z         ctr_y: "f32[3225][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T04:56:16.4313662Z         
2025-03-14T04:56:16.4314062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:56:16.4314581Z         getitem_10: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:56:16.4314909Z         dx: "f32[3225, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T04:56:16.4315149Z         
2025-03-14T04:56:16.4315539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:56:16.4316047Z         getitem_11: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:56:16.4316372Z         dy: "f32[3225, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T04:56:16.4316613Z         
2025-03-14T04:56:16.4317005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:56:16.4317511Z         getitem_12: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:56:16.4317833Z         dw: "f32[3225, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T04:56:16.4318069Z         
2025-03-14T04:56:16.4318492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:56:16.4319032Z         getitem_13: "f32[3225, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:56:16.4319384Z         dh: "f32[3225, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T04:56:16.4319622Z         
2025-03-14T04:56:16.4320049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:56:16.4320582Z         dw_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:56:16.4320844Z         
2025-03-14T04:56:16.4321275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:56:16.4321799Z         dh_1: "f32[3225, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:56:16.4322054Z         
2025-03-14T04:56:16.4322488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:56:16.4323027Z         getitem_14: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:56:16.4323387Z         mul_2: "f32[3225, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T04:56:16.4323725Z         getitem_15: "f32[3225, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:56:16.4324080Z         pred_ctr_x: "f32[3225, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T04:56:16.4324354Z         
2025-03-14T04:56:16.4324787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:56:16.4325326Z         getitem_16: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:56:16.4325645Z         mul_3: "f32[3225, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T04:56:16.4325999Z         getitem_17: "f32[3225, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:56:16.4326348Z         pred_ctr_y: "f32[3225, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T04:56:16.4326612Z         
2025-03-14T04:56:16.4327050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:56:16.4327551Z         exp: "f32[3225, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:56:16.4327876Z         getitem_18: "f32[3225, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:56:16.4328218Z         pred_w: "f32[3225, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T04:56:16.4328471Z         
2025-03-14T04:56:16.4328888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:56:16.4329381Z         exp_1: "f32[3225, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:56:16.4329712Z         getitem_19: "f32[3225, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:56:16.4330055Z         pred_h: "f32[3225, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T04:56:16.4330310Z         
2025-03-14T04:56:16.4330707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:56:16.4331167Z         mul_6: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:56:16.4331455Z         x1: "f32[3225, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:56:16.4331691Z         
2025-03-14T04:56:16.4332069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:56:16.4332519Z         mul_7: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:56:16.4332775Z         y1: "f32[3225, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:56:16.4333008Z         
2025-03-14T04:56:16.4333394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:56:16.4333862Z         mul_8: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:56:16.4334149Z         x2: "f32[3225, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:56:16.4334393Z         
2025-03-14T04:56:16.4334781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:56:16.4335240Z         mul_9: "f32[3225, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:56:16.4335518Z         y2: "f32[3225, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:56:16.4335765Z         
2025-03-14T04:56:16.4336243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:56:16.4336822Z         pred_boxes: "f32[3225, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:56:16.4337140Z         
2025-03-14T04:56:16.4337564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:56:16.4338119Z         predict_boxes: "f32[3225, 320][320, 1]cpu" = pred_boxes.reshape((3225, 320));  pred_boxes = None
2025-03-14T04:56:16.4338413Z         
2025-03-14T04:56:16.4338867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:56:16.4339483Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T04:56:16.4339839Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T04:56:16.4340126Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T04:56:16.4340424Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T04:56:16.4340735Z         getitem_23: "f32[1225 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T04:56:16.4340993Z         
2025-03-14T04:56:16.4341368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:56:16.4341909Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4342248Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T04:56:16.4342485Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T04:56:16.4342845Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:56:16.4343195Z         getitem_26: "Sym(1225 - s0)" = size_3[0]
2025-03-14T04:56:16.4343444Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T04:56:16.4343669Z         
2025-03-14T04:56:16.4344173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:56:16.4344783Z         probs: "f32[3225, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:56:16.4345113Z         
2025-03-14T04:56:16.4345566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:56:16.4346175Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T04:56:16.4346545Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:56:16.4346845Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T04:56:16.4347151Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T04:56:16.4347466Z         getitem_31: "f32[1225 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T04:56:16.4347725Z         
2025-03-14T04:56:16.4348275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:16.4348963Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:56:16.4349334Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:16.4349674Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:56:16.4350024Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:16.4350320Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:16.4350586Z         
2025-03-14T04:56:16.4351031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:16.4351557Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:16.4351798Z         
2025-03-14T04:56:19.2204725Z 
2025-03-14T04:56:19.2207072Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:19.2208251Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:56:19.2213466Z         l_scores_0_ = L_scores_0_
2025-03-14T04:56:19.2215574Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:56:19.2215978Z         
2025-03-14T04:56:19.2216657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:19.2217551Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:56:19.2217911Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:19.2218759Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:56:19.2219101Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:19.2219423Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:19.2219681Z         
2025-03-14T04:56:19.2220162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:19.2220704Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:19.2220957Z         
2025-03-14T04:56:19.2221061Z 
2025-03-14T04:56:19.2221156Z class GraphModule(torch.nn.Module):
2025-03-14T04:56:19.2221681Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T04:56:19.2222017Z         l_scores_0_ = L_scores_0_
2025-03-14T04:56:19.2222250Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:56:19.2222456Z         
2025-03-14T04:56:19.2223063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:56:19.2223802Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:56:19.2224212Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:56:19.2224556Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:56:19.2224892Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:56:19.2225204Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:56:19.2225460Z         
2025-03-14T04:56:19.2225944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:56:19.2226533Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:56:19.2226780Z         
2025-03-14T04:56:35.6810234Z Compilation time (from dynamo_timed): 48.186219551
2025-03-14T04:56:35.6813375Z pass
2025-03-14T04:56:35.6816366Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:56:35.6817445Z TIMING: entire_frame_compile:48.18622 gc:0.03682 _recursive_pre_grad_passes:0.03256 async_compile.wait:11.90747 backend_compile:32.40393 _recursive_joint_graph_passes:0.58521 _recursive_post_grad_passes:0.15726 code_gen:16.70481 inductor_compile:19.37403 total_wall_time:48.18622
2025-03-14T04:56:35.6822759Z STATS: call_* op count: 781 | FakeTensorMode.__torch_dispatch__:27912 | FakeTensor.__torch_dispatch__:3330 | ProxyTorchDispatchMode.__torch_dispatch__:10384 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T04:56:35.6825172Z Dynamo produced 52 graphs covering 781 ops with 42 graph breaks (6 unique)
2025-03-14T04:56:41.5797936Z 
2025-03-14T04:56:48.6479156Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:56:48.6479457Z loading model: 0it [00:07, ?it/s]
2025-03-14T04:56:48.6491778Z cpu  eval  detectron2_fasterrcnn_r_101_fpn    
2025-03-14T04:57:06.4525394Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_101_fpn. Setting accuracy check to cosine
2025-03-14T04:57:06.4746883Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:57:19.7548394Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:57:28.8071114Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:57:45.0816098Z 
2025-03-14T04:57:45.0816855Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:45.0993597Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", 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1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:57:45.1114046Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:45.1114648Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1115595Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1116606Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1117556Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1118567Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1119463Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1120413Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1121387Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1122342Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1123261Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1124170Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1125135Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1126120Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1127094Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1128004Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1128901Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1129808Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1130746Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1131622Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1132441Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1133303Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.1134236Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1135205Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1136114Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.1136951Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.1137794Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1138666Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1139614Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1140523Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1141414Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1142246Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1143132Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1144041Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1145043Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1145921Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1146712Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1147523Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1148419Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1149272Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1150086Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1150864Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1151672Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1152531Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1153366Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1154198Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1154975Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1155802Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1156661Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1157519Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1158333Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1159123Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1159925Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1160790Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1161624Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1162456Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1163236Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1164043Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1164903Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1165740Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1166550Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1167351Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1168165Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1169057Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1169894Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1170735Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1171518Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1172342Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1173204Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1174043Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1174855Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1175654Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.1176516Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1177406Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1178278Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.1179119Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.1179914Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1180723Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1181774Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1182650Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1183537Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1184376Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1185228Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1186171Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1187111Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1188033Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1188884Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1189760Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1190735Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1191666Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1192584Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1193461Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1194376Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1195349Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1196273Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1197092Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1197904Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1198719Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1199590Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1200441Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1201246Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1202024Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1202824Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1203683Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1204517Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1205347Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1206131Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1206953Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1207808Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1208653Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1209466Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1210259Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1211071Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1211967Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1212806Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1213636Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1214414Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1215226Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1216108Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1216942Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1217750Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1218550Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1219362Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1220226Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1221094Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1221978Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1222814Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1223675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1224724Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1225683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1226606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1227443Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1228318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1229221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1230106Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1230958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1231800Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.1232694Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1233659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1234582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.1235477Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.1236322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1237188Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1238087Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1238946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1239766Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1240561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1241370Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1242254Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1243095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1243922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1244707Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1245531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1246399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1247263Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1248071Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1248846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1249657Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1250516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1251350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1252157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1252963Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1253778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1254654Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1255496Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1256323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1257098Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1257905Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1258774Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1259613Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1260427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1261255Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1262130Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1263050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1263943Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1264917Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1265826Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1266743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1267658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1268560Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1269427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1270247Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1271120Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1272030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1272923Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1273790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1274608Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1275422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1276305Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1277142Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1277950Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1278730Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1279549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1280405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1281269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1282271Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1283108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1283916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1284807Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1285646Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1286492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1287272Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1288080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1288935Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1289796Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1290611Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1291402Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1292230Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1293151Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1293996Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1294837Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1295666Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1296545Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1297482Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1298391Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1299289Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1300136Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1301020Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1301961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1302872Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1303753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1304725Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1305676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1306551Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1307387Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1308196Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1308971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1309797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1310663Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1311523Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1312343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1313137Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1313949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1314810Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1315659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1316470Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1317245Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1318054Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1318925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1319783Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1320606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1321368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1322158Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1323004Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1323863Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1324672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1325483Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1326296Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1327169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1327982Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1328790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1329574Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1330378Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1331236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1332101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1332909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1333693Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1334494Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1335356Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1336193Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1337020Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1337798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1338631Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1339489Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1340324Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1341155Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1341936Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1342745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1343608Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1344560Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1345418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1346225Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1347037Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1347907Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1348753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1349568Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1350362Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1351210Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1352081Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1352946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1353767Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1354572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1355367Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1356214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1357052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1357846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1358611Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1359430Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1360280Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1361110Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1361904Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1362669Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1363461Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1364320Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1365146Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1365961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1366720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1367517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1368369Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1369190Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1369989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1370751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1371554Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1372405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1373241Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1374040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1374812Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1375610Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1376463Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1377289Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1378116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1378878Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1379687Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1380526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1381368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1382310Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1383103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1383929Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1384854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1385704Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1386576Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1387454Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1388389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1389303Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1390208Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1391075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1391974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1392878Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1393811Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1394649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1395467Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1396286Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1397095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1397961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1398802Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1399624Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1400410Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1401221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1402086Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1402922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1403738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1404519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1405330Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1406207Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1407030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1407859Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1408636Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1409466Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1410329Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1411175Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1411993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1412777Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1413573Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1414445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1415278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1416083Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1416851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1417653Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1418505Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1419351Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1420170Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1420964Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1421772Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1422638Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1423509Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1424446Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1425285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1426125Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1426990Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1427833Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1428672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1429455Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1430278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1431139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1431977Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1432791Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1433593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1434412Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1435293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1436136Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1436971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1437750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1438576Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1439440Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1440282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1441113Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1441916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1442728Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1443602Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1444444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1445260Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1446040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1446902Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1447769Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1448633Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1449450Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1450247Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1451060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1451930Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1452779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1453598Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1454390Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1455199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1456080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1456925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1457740Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1458519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1459341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1460201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1461064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1461885Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1462746Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1463628Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1464681Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1465635Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1466515Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1467365Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1468262Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1469186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1470117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1471004Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1471846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1472709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1473625Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1474512Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1475392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1476203Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1477043Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1477910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1478788Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1479610Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1480405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1481220Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1482198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1483052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1483875Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1484712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1485549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1486425Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1487282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1488118Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1488904Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1489748Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1490619Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1491486Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1492306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1493133Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1493985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1494868Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1495736Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1496614Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1497723Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1498633Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1499556Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1500458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1501327Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1502157Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1503014Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1503946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1504936Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1505843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1506667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1507539Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1508440Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1509288Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1510106Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1510897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1511722Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1512625Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1513472Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1514290Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1515074Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1515884Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1516745Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1517593Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1518418Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1519202Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1520031Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1520893Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1521755Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1522572Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1523350Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1524164Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1525007Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1525831Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1526641Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1527423Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.1528255Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1529149Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1530022Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.1530844Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.1531666Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1532453Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1533509Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1534474Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1535322Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1536103Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1536916Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1537776Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1538618Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1539655Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1540783Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1542100Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1543497Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1544433Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1545294Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1546129Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.1546994Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1547922Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1549068Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.1550111Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.1550906Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.1551753Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1552633Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1553499Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.1554339Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.1555125Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.1555933Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.1556814Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.1557651Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.1558467Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.1559142Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:57:45.1559664Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:57:45.1560188Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:57:45.1560696Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:57:45.1561204Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:57:45.1561716Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:57:45.1562241Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:57:45.1562745Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:57:45.1563268Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:57:45.1563776Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:57:45.1564276Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:57:45.1564775Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:57:45.1565308Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:57:45.1565819Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:57:45.1566336Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:57:45.1566826Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:57:45.1567451Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:45.1568215Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:57:45.1568964Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:57:45.1569708Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:57:45.1570467Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:57:45.1571184Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:45.1571873Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:45.1572609Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:45.1573387Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:45.1574153Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:45.1574899Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:45.1575371Z         
2025-03-14T04:57:45.1575793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1576674Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1577337Z         
2025-03-14T04:57:45.1577712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1580431Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1582838Z         
2025-03-14T04:57:45.1583280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:45.1583825Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:45.1584169Z         
2025-03-14T04:57:45.1584692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:45.1585434Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:45.1585827Z         
2025-03-14T04:57:45.1586196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1587153Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1587829Z         
2025-03-14T04:57:45.1588230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1590606Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1592717Z         
2025-03-14T04:57:45.1593144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1593734Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:45.1594031Z         
2025-03-14T04:57:45.1594393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1595249Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1595920Z         
2025-03-14T04:57:45.1596297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1598511Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1600490Z         
2025-03-14T04:57:45.1600887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1601402Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:45.1601682Z         
2025-03-14T04:57:45.1602059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1602864Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1603465Z         
2025-03-14T04:57:45.1603812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1605852Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1607677Z         
2025-03-14T04:57:45.1608028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1608824Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.1609428Z         
2025-03-14T04:57:45.1609791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1611888Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1613931Z         
2025-03-14T04:57:45.1614288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1614758Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:45.1615016Z         
2025-03-14T04:57:45.1615404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1615903Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:45.1616181Z         
2025-03-14T04:57:45.1616524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1617324Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1617930Z         
2025-03-14T04:57:45.1618288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1620835Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1622856Z         
2025-03-14T04:57:45.1623257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1623771Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:45.1624057Z         
2025-03-14T04:57:45.1624530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1625459Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1626130Z         
2025-03-14T04:57:45.1626505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1628662Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1630540Z         
2025-03-14T04:57:45.1630910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1631398Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:45.1631662Z         
2025-03-14T04:57:45.1632026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1632835Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1633463Z         
2025-03-14T04:57:45.1633816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1635932Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1637824Z         
2025-03-14T04:57:45.1638192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1638702Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:45.1638979Z         
2025-03-14T04:57:45.1639349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1639840Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:45.1640121Z         
2025-03-14T04:57:45.1640461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1641248Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1641832Z         
2025-03-14T04:57:45.1642177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1644229Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1646054Z         
2025-03-14T04:57:45.1646418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1646891Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:45.1647147Z         
2025-03-14T04:57:45.1647482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1648262Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1648896Z         
2025-03-14T04:57:45.1649239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1651277Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1653128Z         
2025-03-14T04:57:45.1653486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1653957Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:45.1654213Z         
2025-03-14T04:57:45.1654542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1655327Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1655932Z         
2025-03-14T04:57:45.1656269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1658342Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1660501Z         
2025-03-14T04:57:45.1660876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1661380Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:45.1661662Z         
2025-03-14T04:57:45.1662040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1662650Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:45.1663077Z         
2025-03-14T04:57:45.1663554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1664679Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1665526Z         
2025-03-14T04:57:45.1665913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1668125Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1670190Z         
2025-03-14T04:57:45.1670583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1671105Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:45.1671387Z         
2025-03-14T04:57:45.1671745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1672640Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1673280Z         
2025-03-14T04:57:45.1673649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1675796Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1677669Z         
2025-03-14T04:57:45.1678071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1678568Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:45.1678839Z         
2025-03-14T04:57:45.1679179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1680012Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1680631Z         
2025-03-14T04:57:45.1680981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1683276Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1685192Z         
2025-03-14T04:57:45.1685539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1686357Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.1686984Z         
2025-03-14T04:57:45.1687398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1689549Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1691648Z         
2025-03-14T04:57:45.1692020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1692544Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:45.1692819Z         
2025-03-14T04:57:45.1693186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1693709Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:45.1693983Z         
2025-03-14T04:57:45.1694441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1695386Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1696221Z         
2025-03-14T04:57:45.1696777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1699549Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1701891Z         
2025-03-14T04:57:45.1702380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1703217Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:45.1703571Z         
2025-03-14T04:57:45.1703960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1705041Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1705729Z         
2025-03-14T04:57:45.1706127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1708675Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1710683Z         
2025-03-14T04:57:45.1711080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1711599Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:45.1711884Z         
2025-03-14T04:57:45.1712238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1713879Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1714611Z         
2025-03-14T04:57:45.1714974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1717154Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1719293Z         
2025-03-14T04:57:45.1719704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1720228Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:45.1720524Z         
2025-03-14T04:57:45.1720917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1721434Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:45.1721718Z         
2025-03-14T04:57:45.1722066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1722888Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1723505Z         
2025-03-14T04:57:45.1723864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1726027Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1727980Z         
2025-03-14T04:57:45.1728362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1728856Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:45.1729126Z         
2025-03-14T04:57:45.1729475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1730298Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1730924Z         
2025-03-14T04:57:45.1731282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1733462Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1735384Z         
2025-03-14T04:57:45.1735756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1736237Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:45.1736506Z         
2025-03-14T04:57:45.1736842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1737883Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1738565Z         
2025-03-14T04:57:45.1738939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1741878Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1744070Z         
2025-03-14T04:57:45.1744677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1745218Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:45.1745542Z         
2025-03-14T04:57:45.1745970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1746526Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:45.1746863Z         
2025-03-14T04:57:45.1747372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1748629Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1749564Z         
2025-03-14T04:57:45.1750087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1753445Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1756359Z         
2025-03-14T04:57:45.1756764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1757279Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:45.1757566Z         
2025-03-14T04:57:45.1757928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1759033Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1760024Z         
2025-03-14T04:57:45.1760543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1763454Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1765640Z         
2025-03-14T04:57:45.1766037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1766555Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:45.1766838Z         
2025-03-14T04:57:45.1767198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1768050Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1768709Z         
2025-03-14T04:57:45.1769099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1771316Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1773337Z         
2025-03-14T04:57:45.1773726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1774258Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:45.1774550Z         
2025-03-14T04:57:45.1774940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1775462Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:45.1775762Z         
2025-03-14T04:57:45.1776119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1776959Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1777613Z         
2025-03-14T04:57:45.1777982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1780169Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1782280Z         
2025-03-14T04:57:45.1782680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1783192Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:45.1783472Z         
2025-03-14T04:57:45.1783881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1784876Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1785576Z         
2025-03-14T04:57:45.1785970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1788212Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1790186Z         
2025-03-14T04:57:45.1790579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1791113Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:45.1791389Z         
2025-03-14T04:57:45.1791762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1792648Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1793370Z         
2025-03-14T04:57:45.1793756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1795958Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1797933Z         
2025-03-14T04:57:45.1798293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1799158Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.1799807Z         
2025-03-14T04:57:45.1800182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1802504Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1804628Z         
2025-03-14T04:57:45.1804997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1805498Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:45.1805761Z         
2025-03-14T04:57:45.1806131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1806613Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:45.1806879Z         
2025-03-14T04:57:45.1807216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1808031Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1808629Z         
2025-03-14T04:57:45.1808982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1811075Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1812966Z         
2025-03-14T04:57:45.1813342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1813814Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:45.1814076Z         
2025-03-14T04:57:45.1814417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1815210Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1815822Z         
2025-03-14T04:57:45.1816176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1818313Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1820200Z         
2025-03-14T04:57:45.1820577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1821073Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:45.1821376Z         
2025-03-14T04:57:45.1821721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1822556Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1823205Z         
2025-03-14T04:57:45.1823585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1825978Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1827951Z         
2025-03-14T04:57:45.1828341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1828852Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:45.1829134Z         
2025-03-14T04:57:45.1829518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1830026Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:45.1830305Z         
2025-03-14T04:57:45.1830663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1831489Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1832119Z         
2025-03-14T04:57:45.1832502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1834730Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1836646Z         
2025-03-14T04:57:45.1837019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1837492Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:45.1837751Z         
2025-03-14T04:57:45.1838088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1838876Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1839480Z         
2025-03-14T04:57:45.1839833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1841962Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1843830Z         
2025-03-14T04:57:45.1844204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1844687Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:45.1844944Z         
2025-03-14T04:57:45.1845282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1846099Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1846709Z         
2025-03-14T04:57:45.1847061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1849172Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1851052Z         
2025-03-14T04:57:45.1851420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1851906Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:45.1852172Z         
2025-03-14T04:57:45.1852541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1853027Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:45.1853292Z         
2025-03-14T04:57:45.1853629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1854417Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1855035Z         
2025-03-14T04:57:45.1855391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1857489Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1859389Z         
2025-03-14T04:57:45.1859764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1860259Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:45.1860523Z         
2025-03-14T04:57:45.1860862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1861656Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1862276Z         
2025-03-14T04:57:45.1862632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1864834Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1866843Z         
2025-03-14T04:57:45.1867241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1867749Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:45.1868025Z         
2025-03-14T04:57:45.1868385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1869244Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1869881Z         
2025-03-14T04:57:45.1870257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1872477Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1874445Z         
2025-03-14T04:57:45.1874858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1875375Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:45.1875683Z         
2025-03-14T04:57:45.1876096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1876613Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:45.1876895Z         
2025-03-14T04:57:45.1877255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1878101Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1878733Z         
2025-03-14T04:57:45.1879087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1881188Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1883188Z         
2025-03-14T04:57:45.1883612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1884106Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:45.1884378Z         
2025-03-14T04:57:45.1884739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1885574Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1886214Z         
2025-03-14T04:57:45.1886584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1888775Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1890737Z         
2025-03-14T04:57:45.1891118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1891604Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:45.1891873Z         
2025-03-14T04:57:45.1892228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1893112Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1893783Z         
2025-03-14T04:57:45.1894162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1896354Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1898377Z         
2025-03-14T04:57:45.1898765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1899282Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:45.1899568Z         
2025-03-14T04:57:45.1899961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1900472Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:45.1900745Z         
2025-03-14T04:57:45.1901104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1901943Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1902577Z         
2025-03-14T04:57:45.1902951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1905303Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1907367Z         
2025-03-14T04:57:45.1907741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1908213Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:45.1908472Z         
2025-03-14T04:57:45.1908801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1909583Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1910178Z         
2025-03-14T04:57:45.1910523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1912609Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1914478Z         
2025-03-14T04:57:45.1914849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1915322Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:45.1915582Z         
2025-03-14T04:57:45.1915923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1916715Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1917319Z         
2025-03-14T04:57:45.1917690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1919788Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1921679Z         
2025-03-14T04:57:45.1922044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1922523Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:45.1922785Z         
2025-03-14T04:57:45.1923154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1923638Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:45.1923903Z         
2025-03-14T04:57:45.1924242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1925049Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1925640Z         
2025-03-14T04:57:45.1926010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1928094Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1929986Z         
2025-03-14T04:57:45.1930359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1930841Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:57:45.1931103Z         
2025-03-14T04:57:45.1931463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1932258Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1932888Z         
2025-03-14T04:57:45.1933244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1935333Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1937218Z         
2025-03-14T04:57:45.1937592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1938064Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:57:45.1938324Z         
2025-03-14T04:57:45.1938659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1939451Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1940077Z         
2025-03-14T04:57:45.1940430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1942528Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1944549Z         
2025-03-14T04:57:45.1944963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1945519Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:57:45.1945786Z         
2025-03-14T04:57:45.1946157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1946660Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:57:45.1946921Z         
2025-03-14T04:57:45.1947260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1948043Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1948660Z         
2025-03-14T04:57:45.1949011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1951094Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1952959Z         
2025-03-14T04:57:45.1953321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1953795Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:57:45.1954077Z         
2025-03-14T04:57:45.1954417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1955209Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1955821Z         
2025-03-14T04:57:45.1956170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1958288Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1960167Z         
2025-03-14T04:57:45.1960541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1961018Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:57:45.1961275Z         
2025-03-14T04:57:45.1961612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1962408Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1963039Z         
2025-03-14T04:57:45.1963397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1965501Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1967382Z         
2025-03-14T04:57:45.1967695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1967855Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:57:45.1967921Z         
2025-03-14T04:57:45.1968215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1968364Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:57:45.1968438Z         
2025-03-14T04:57:45.1968690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1969197Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1969264Z         
2025-03-14T04:57:45.1969536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1971357Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1971440Z         
2025-03-14T04:57:45.1971752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1971896Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:57:45.1971970Z         
2025-03-14T04:57:45.1972222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1972723Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1972787Z         
2025-03-14T04:57:45.1973061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1974850Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1974917Z         
2025-03-14T04:57:45.1975205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1975341Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:57:45.1975412Z         
2025-03-14T04:57:45.1975659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1976146Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1976217Z         
2025-03-14T04:57:45.1976475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1978247Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1978350Z         
2025-03-14T04:57:45.1978631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1978794Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:57:45.1978860Z         
2025-03-14T04:57:45.1979149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1979292Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:57:45.1979364Z         
2025-03-14T04:57:45.1979615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1980102Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1980170Z         
2025-03-14T04:57:45.1980444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1982364Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1982451Z         
2025-03-14T04:57:45.1982747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1982887Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:57:45.1982962Z         
2025-03-14T04:57:45.1983213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1983736Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1983826Z         
2025-03-14T04:57:45.1984163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1986145Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1986257Z         
2025-03-14T04:57:45.1986569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1986714Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:57:45.1986795Z         
2025-03-14T04:57:45.1987059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1987583Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1987653Z         
2025-03-14T04:57:45.1987957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1989826Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1989893Z         
2025-03-14T04:57:45.1990183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.1990335Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:57:45.1990409Z         
2025-03-14T04:57:45.1990704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1990853Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:57:45.1990937Z         
2025-03-14T04:57:45.1991192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1991678Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.1991762Z         
2025-03-14T04:57:45.1992050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1993895Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1993972Z         
2025-03-14T04:57:45.1994263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1994401Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:57:45.1994472Z         
2025-03-14T04:57:45.1994738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1995227Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.1995295Z         
2025-03-14T04:57:45.1995570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.1997360Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.1997439Z         
2025-03-14T04:57:45.1997733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.1997885Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:57:45.1997964Z         
2025-03-14T04:57:45.1998212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.1998707Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.1998787Z         
2025-03-14T04:57:45.1999057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2000846Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2000916Z         
2025-03-14T04:57:45.2001201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2001365Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:57:45.2001440Z         
2025-03-14T04:57:45.2001720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2001870Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:57:45.2001936Z         
2025-03-14T04:57:45.2002218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2004697Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2004836Z         
2025-03-14T04:57:45.2005140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2007000Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2007106Z         
2025-03-14T04:57:45.2007396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2007566Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:57:45.2007642Z         
2025-03-14T04:57:45.2007896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2008398Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2008466Z         
2025-03-14T04:57:45.2008751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2010603Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2010684Z         
2025-03-14T04:57:45.2010987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2011125Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:57:45.2011202Z         
2025-03-14T04:57:45.2011456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2011971Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2012039Z         
2025-03-14T04:57:45.2012318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2014169Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2014277Z         
2025-03-14T04:57:45.2014577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2014731Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:57:45.2014806Z         
2025-03-14T04:57:45.2015097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2015249Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:57:45.2015316Z         
2025-03-14T04:57:45.2015580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2016081Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2016156Z         
2025-03-14T04:57:45.2016427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2018284Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2018363Z         
2025-03-14T04:57:45.2018656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2018806Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:57:45.2018871Z         
2025-03-14T04:57:45.2019135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2019653Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2019730Z         
2025-03-14T04:57:45.2020027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2021818Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2021911Z         
2025-03-14T04:57:45.2022200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2022337Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:57:45.2022410Z         
2025-03-14T04:57:45.2022658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2023162Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2023229Z         
2025-03-14T04:57:45.2023533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2025568Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2025658Z         
2025-03-14T04:57:45.2025970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2026119Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:57:45.2026195Z         
2025-03-14T04:57:45.2026498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2026650Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:57:45.2026735Z         
2025-03-14T04:57:45.2026994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2027472Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2027551Z         
2025-03-14T04:57:45.2027837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2029636Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2029715Z         
2025-03-14T04:57:45.2029999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2030144Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:57:45.2030211Z         
2025-03-14T04:57:45.2030483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2030968Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2031042Z         
2025-03-14T04:57:45.2031305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2033092Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2033173Z         
2025-03-14T04:57:45.2033454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2033611Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:57:45.2033676Z         
2025-03-14T04:57:45.2033936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2034431Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2034520Z         
2025-03-14T04:57:45.2034790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2036581Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2036655Z         
2025-03-14T04:57:45.2036942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2037108Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:57:45.2037184Z         
2025-03-14T04:57:45.2037465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2037616Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:57:45.2037683Z         
2025-03-14T04:57:45.2037943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2038423Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2038499Z         
2025-03-14T04:57:45.2038763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2040594Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2040684Z         
2025-03-14T04:57:45.2040967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2041133Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:57:45.2041198Z         
2025-03-14T04:57:45.2041456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2041942Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2042017Z         
2025-03-14T04:57:45.2042280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2044077Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2044153Z         
2025-03-14T04:57:45.2044437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2044580Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:57:45.2044646Z         
2025-03-14T04:57:45.2044901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2045390Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2045465Z         
2025-03-14T04:57:45.2045724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2047525Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2047665Z         
2025-03-14T04:57:45.2047942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2048101Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:57:45.2048165Z         
2025-03-14T04:57:45.2048453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2048595Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:57:45.2048667Z         
2025-03-14T04:57:45.2048911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2049395Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2049469Z         
2025-03-14T04:57:45.2049731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2051510Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2051587Z         
2025-03-14T04:57:45.2051878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2052022Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:57:45.2052086Z         
2025-03-14T04:57:45.2052345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2052840Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2052914Z         
2025-03-14T04:57:45.2053195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2054987Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2055078Z         
2025-03-14T04:57:45.2055364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2055509Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:57:45.2055575Z         
2025-03-14T04:57:45.2055833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2056315Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2056393Z         
2025-03-14T04:57:45.2056673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2058464Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2058542Z         
2025-03-14T04:57:45.2058825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2058983Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:57:45.2059051Z         
2025-03-14T04:57:45.2059357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2059499Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:57:45.2059575Z         
2025-03-14T04:57:45.2059837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2060322Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2060395Z         
2025-03-14T04:57:45.2060673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2062478Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2062549Z         
2025-03-14T04:57:45.2062840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2062985Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:57:45.2063052Z         
2025-03-14T04:57:45.2063330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2063823Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2063905Z         
2025-03-14T04:57:45.2064257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2066256Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2066342Z         
2025-03-14T04:57:45.2066664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2066839Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:57:45.2066910Z         
2025-03-14T04:57:45.2067205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2067755Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2067858Z         
2025-03-14T04:57:45.2068154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2070155Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2070242Z         
2025-03-14T04:57:45.2070557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2070744Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:57:45.2070818Z         
2025-03-14T04:57:45.2071149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2071307Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:57:45.2071390Z         
2025-03-14T04:57:45.2071675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2072178Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2072244Z         
2025-03-14T04:57:45.2072517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2074326Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2074408Z         
2025-03-14T04:57:45.2074702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2074854Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:57:45.2074929Z         
2025-03-14T04:57:45.2075179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2075675Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2075748Z         
2025-03-14T04:57:45.2076009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2077811Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2077891Z         
2025-03-14T04:57:45.2078175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2078317Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:57:45.2078381Z         
2025-03-14T04:57:45.2078635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2079119Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2079191Z         
2025-03-14T04:57:45.2079453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2081244Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2081348Z         
2025-03-14T04:57:45.2081822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2081986Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:57:45.2082053Z         
2025-03-14T04:57:45.2082348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2082491Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:57:45.2082564Z         
2025-03-14T04:57:45.2082814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2083299Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2083365Z         
2025-03-14T04:57:45.2083635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2085461Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2085533Z         
2025-03-14T04:57:45.2085822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2085969Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:57:45.2086044Z         
2025-03-14T04:57:45.2086291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2086823Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2086891Z         
2025-03-14T04:57:45.2087165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2089079Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2089178Z         
2025-03-14T04:57:45.2089493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2089646Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:57:45.2089723Z         
2025-03-14T04:57:45.2089993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2090501Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2090576Z         
2025-03-14T04:57:45.2090843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2092664Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2092743Z         
2025-03-14T04:57:45.2093020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2093183Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:57:45.2093249Z         
2025-03-14T04:57:45.2093557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2093707Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:57:45.2093780Z         
2025-03-14T04:57:45.2094044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2094533Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2094598Z         
2025-03-14T04:57:45.2094884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2096669Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2096746Z         
2025-03-14T04:57:45.2097036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2097174Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:57:45.2097247Z         
2025-03-14T04:57:45.2097511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2098010Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2098077Z         
2025-03-14T04:57:45.2098347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2100149Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2100217Z         
2025-03-14T04:57:45.2100504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2100657Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:57:45.2100731Z         
2025-03-14T04:57:45.2100983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2101488Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2101571Z         
2025-03-14T04:57:45.2101843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2103624Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2103693Z         
2025-03-14T04:57:45.2103978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2104218Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:57:45.2104302Z         
2025-03-14T04:57:45.2104630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2104807Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:57:45.2104886Z         
2025-03-14T04:57:45.2105181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2105745Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2105821Z         
2025-03-14T04:57:45.2106120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2108144Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2108246Z         
2025-03-14T04:57:45.2108579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2108753Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:57:45.2108836Z         
2025-03-14T04:57:45.2109122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2109694Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2109769Z         
2025-03-14T04:57:45.2110078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2112158Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2112251Z         
2025-03-14T04:57:45.2112542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2112680Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:57:45.2112754Z         
2025-03-14T04:57:45.2113001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2113500Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2113568Z         
2025-03-14T04:57:45.2113836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2115628Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2116367Z         
2025-03-14T04:57:45.2116656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2116819Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:57:45.2116896Z         
2025-03-14T04:57:45.2117179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2117335Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:57:45.2117401Z         
2025-03-14T04:57:45.2117657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2118135Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2118211Z         
2025-03-14T04:57:45.2118483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2120278Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2120357Z         
2025-03-14T04:57:45.2120643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2120791Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:57:45.2120865Z         
2025-03-14T04:57:45.2121114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2121632Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2121699Z         
2025-03-14T04:57:45.2121987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2123765Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2123857Z         
2025-03-14T04:57:45.2124152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2124288Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:57:45.2124361Z         
2025-03-14T04:57:45.2124613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2125114Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2125182Z         
2025-03-14T04:57:45.2125471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2127246Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2127314Z         
2025-03-14T04:57:45.2127601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2127758Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:57:45.2127856Z         
2025-03-14T04:57:45.2128154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2128305Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:57:45.2128371Z         
2025-03-14T04:57:45.2128644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2129135Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2129208Z         
2025-03-14T04:57:45.2129494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2131271Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2131349Z         
2025-03-14T04:57:45.2131633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2131778Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:57:45.2131845Z         
2025-03-14T04:57:45.2132122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2132611Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2132686Z         
2025-03-14T04:57:45.2132958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2134731Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2134809Z         
2025-03-14T04:57:45.2135099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2135253Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:57:45.2135326Z         
2025-03-14T04:57:45.2135578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2136083Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2136171Z         
2025-03-14T04:57:45.2136441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2138232Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2138307Z         
2025-03-14T04:57:45.2138594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2138766Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:57:45.2138841Z         
2025-03-14T04:57:45.2139124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2139276Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:57:45.2139344Z         
2025-03-14T04:57:45.2139602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2140084Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2140159Z         
2025-03-14T04:57:45.2140423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2142222Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2142315Z         
2025-03-14T04:57:45.2142600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2142766Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:57:45.2142832Z         
2025-03-14T04:57:45.2143089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2143579Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2143653Z         
2025-03-14T04:57:45.2143918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2145845Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2145932Z         
2025-03-14T04:57:45.2146241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2146388Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:57:45.2146454Z         
2025-03-14T04:57:45.2146724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2147254Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2147326Z         
2025-03-14T04:57:45.2147614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2149494Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2149609Z         
2025-03-14T04:57:45.2149884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2150403Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.2150487Z         
2025-03-14T04:57:45.2150763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2152728Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2152810Z         
2025-03-14T04:57:45.2153104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2153272Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:57:45.2153344Z         
2025-03-14T04:57:45.2153647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2153799Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:57:45.2153878Z         
2025-03-14T04:57:45.2154141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2154623Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2154689Z         
2025-03-14T04:57:45.2155001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2156716Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2156810Z         
2025-03-14T04:57:45.2157093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2157227Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:57:45.2157300Z         
2025-03-14T04:57:45.2157544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2158021Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2158087Z         
2025-03-14T04:57:45.2158353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2160087Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2160155Z         
2025-03-14T04:57:45.2160439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2160572Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:57:45.2160645Z         
2025-03-14T04:57:45.2160886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2161384Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2161457Z         
2025-03-14T04:57:45.2161711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2163460Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2163548Z         
2025-03-14T04:57:45.2163820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2163981Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:57:45.2164045Z         
2025-03-14T04:57:45.2164329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2164472Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:57:45.2164547Z         
2025-03-14T04:57:45.2164790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2165259Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.2165345Z         
2025-03-14T04:57:45.2165609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2167342Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2167419Z         
2025-03-14T04:57:45.2167700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2167847Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:57:45.2167919Z         
2025-03-14T04:57:45.2168160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2168665Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.2168730Z         
2025-03-14T04:57:45.2169005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2170985Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2171065Z         
2025-03-14T04:57:45.2171360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2171491Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:57:45.2171563Z         
2025-03-14T04:57:45.2171809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2172316Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.2172380Z         
2025-03-14T04:57:45.2172644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.2174387Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.2174454Z         
2025-03-14T04:57:45.2174745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.2174899Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:57:45.2174989Z         
2025-03-14T04:57:45.2175265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.2175412Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:57:45.2175476Z         
2025-03-14T04:57:45.2175726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2176305Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.2176369Z         
2025-03-14T04:57:45.2176618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2177146Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.2177217Z         
2025-03-14T04:57:45.2177614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.2177888Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:57:45.2177951Z         
2025-03-14T04:57:45.2178205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2178776Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.2178852Z         
2025-03-14T04:57:45.2179199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.2179401Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:57:45.2179476Z         
2025-03-14T04:57:45.2179723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2180302Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.2180368Z         
2025-03-14T04:57:45.2180793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.2181119Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:57:45.2181209Z         
2025-03-14T04:57:45.2181542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2182133Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.2182242Z         
2025-03-14T04:57:45.2182595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.2182804Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:57:45.2182879Z         
2025-03-14T04:57:45.2183128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2183710Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.2183784Z         
2025-03-14T04:57:45.2184228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.2184605Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:57:45.2184678Z         
2025-03-14T04:57:45.2184989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2185631Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.2185710Z         
2025-03-14T04:57:45.2186078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.2186309Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:57:45.2186378Z         
2025-03-14T04:57:45.2186635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2187252Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.2187319Z         
2025-03-14T04:57:45.2187709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:57:45.2187924Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:57:45.2188020Z         
2025-03-14T04:57:45.2188457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2188621Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.2188685Z         
2025-03-14T04:57:45.2188987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2189155Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:45.2189228Z         
2025-03-14T04:57:45.2189662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2189827Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.2189894Z         
2025-03-14T04:57:45.2190197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2190339Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:45.2190417Z         
2025-03-14T04:57:45.2190792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.2190982Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:45.2191084Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:45.2191217Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:45.2191282Z         
2025-03-14T04:57:45.2191631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.2191767Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:45.2191842Z         
2025-03-14T04:57:45.2192163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.2192300Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:45.2192363Z         
2025-03-14T04:57:45.2192747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.2192962Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:45.2193034Z         
2025-03-14T04:57:45.2193456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.2193583Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:45.2194028Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:45.2194158Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:45.2194285Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:57:45.2194367Z         
2025-03-14T04:57:45.2194809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2194961Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.2195034Z         
2025-03-14T04:57:45.2195325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2195495Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:45.2195560Z         
2025-03-14T04:57:45.2195990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2196137Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.2196213Z         
2025-03-14T04:57:45.2196505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2196648Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:57:45.2196713Z         
2025-03-14T04:57:45.2197078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.2197268Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:57:45.2197378Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:57:45.2197502Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:57:45.2197574Z         
2025-03-14T04:57:45.2197908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.2198045Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:57:45.2198111Z         
2025-03-14T04:57:45.2198429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.2198555Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:57:45.2198626Z         
2025-03-14T04:57:45.2198987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.2199209Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:57:45.2199275Z         
2025-03-14T04:57:45.2199705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.2199838Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:57:45.2200255Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:57:45.2200389Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:57:45.2200520Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:57:45.2200591Z         
2025-03-14T04:57:45.2201005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2201158Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.2201238Z         
2025-03-14T04:57:45.2201529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2201662Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:57:45.2201731Z         
2025-03-14T04:57:45.2202141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2202289Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.2202353Z         
2025-03-14T04:57:45.2202639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2202770Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:57:45.2202843Z         
2025-03-14T04:57:45.2203203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.2203400Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:57:45.2203502Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:57:45.2203630Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:57:45.2203693Z         
2025-03-14T04:57:45.2204032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.2204156Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:57:45.2204228Z         
2025-03-14T04:57:45.2204543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.2204671Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:57:45.2204734Z         
2025-03-14T04:57:45.2205109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.2205316Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:57:45.2205386Z         
2025-03-14T04:57:45.2205789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.2205913Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:57:45.2206337Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:57:45.2206475Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:57:45.2206600Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:57:45.2206665Z         
2025-03-14T04:57:45.2207099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2207245Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.2207338Z         
2025-03-14T04:57:45.2207629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2207770Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:57:45.2207835Z         
2025-03-14T04:57:45.2208270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2208413Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.2208485Z         
2025-03-14T04:57:45.2208785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2208922Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:57:45.2208985Z         
2025-03-14T04:57:45.2209347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.2209534Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:57:45.2209642Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:57:45.2209773Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:57:45.2209844Z         
2025-03-14T04:57:45.2210166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.2210299Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:57:45.2210364Z         
2025-03-14T04:57:45.2210698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.2210814Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:57:45.2210884Z         
2025-03-14T04:57:45.2211247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.2211459Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:57:45.2211522Z         
2025-03-14T04:57:45.2211930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.2212078Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:57:45.2212489Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:57:45.2212643Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:57:45.2212761Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:57:45.2212835Z         
2025-03-14T04:57:45.2213258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2213425Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.2213490Z         
2025-03-14T04:57:45.2213791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2213926Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:57:45.2214002Z         
2025-03-14T04:57:45.2214430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.2214578Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.2214643Z         
2025-03-14T04:57:45.2214940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2215078Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:57:45.2215152Z         
2025-03-14T04:57:45.2215519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.2215721Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:57:45.2215821Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:57:45.2215966Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:57:45.2216033Z         
2025-03-14T04:57:45.2216360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.2216488Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:57:45.2216562Z         
2025-03-14T04:57:45.2216883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.2217013Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:57:45.2217080Z         
2025-03-14T04:57:45.2217464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.2217671Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:57:45.2217745Z         
2025-03-14T04:57:45.2218150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.2218304Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:57:45.2218741Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:57:45.2218879Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:57:45.2219001Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:57:45.2219066Z         
2025-03-14T04:57:45.2219368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.2219513Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:57:45.2219652Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:57:45.2219778Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:57:45.2219905Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:57:45.2220023Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:57:45.2220097Z         
2025-03-14T04:57:45.2220359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2220864Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:57:45.2220933Z         
2025-03-14T04:57:45.2221215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.2221412Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:57:45.2221487Z         
2025-03-14T04:57:45.2221885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.2222447Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.2222520Z         
2025-03-14T04:57:45.2222909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.2223464Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:57:45.2223535Z         
2025-03-14T04:57:45.2223811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2224428Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:57:45.2224516Z         
2025-03-14T04:57:45.2224844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.2225073Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:57:45.2225168Z         
2025-03-14T04:57:45.2225588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.2226136Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.2226233Z         
2025-03-14T04:57:45.2226605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.2227148Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:57:45.2227225Z         
2025-03-14T04:57:45.2227489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2227988Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:57:45.2228056Z         
2025-03-14T04:57:45.2228350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.2228544Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:57:45.2228623Z         
2025-03-14T04:57:45.2229028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.2229569Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.2229640Z         
2025-03-14T04:57:45.2230019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.2230543Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:57:45.2230621Z         
2025-03-14T04:57:45.2230894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2231377Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:57:45.2231452Z         
2025-03-14T04:57:45.2231747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.2231951Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:57:45.2232036Z         
2025-03-14T04:57:45.2232432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.2232951Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.2233046Z         
2025-03-14T04:57:45.2233415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.2233945Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:57:45.2234015Z         
2025-03-14T04:57:45.2234290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.2235093Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:45.2235165Z         
2025-03-14T04:57:45.2235461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.2235649Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:57:45.2235725Z         
2025-03-14T04:57:45.2236133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.2237033Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:45.2237104Z         
2025-03-14T04:57:45.2237486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.2238364Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:45.2238437Z         
2025-03-14T04:57:45.2238805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:45.2238979Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:45.2239160Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:45.2239332Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:57:45.2239494Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:57:45.2239661Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:57:45.2239844Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:57:45.2240000Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:57:45.2240151Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:57:45.2240311Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:57:45.2240453Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:57:45.2240520Z         
2025-03-14T04:57:45.2240943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:45.2241121Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:57:45.2241314Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:57:45.2241494Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:57:45.2241666Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:57:45.2241840Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:57:45.2242037Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:57:45.2242197Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:57:45.2242364Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:57:45.2242539Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:57:45.2242684Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:57:45.2242851Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:57:45.2243017Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:57:45.2243169Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:57:45.2243325Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:57:45.2243497Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:57:45.2243567Z         
2025-03-14T04:57:45.2243993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.2244200Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:45.2244287Z         
2025-03-14T04:57:45.2244725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.2244894Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:45.2245043Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:45.2245211Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:45.2245279Z         
2025-03-14T04:57:45.2245662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.2245832Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:45.2245911Z         
2025-03-14T04:57:45.2246225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.2246377Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:45.2246442Z         
2025-03-14T04:57:45.2246762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.2246899Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:45.2247033Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2247185Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:57:45.2247258Z         
2025-03-14T04:57:45.2247575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.2247726Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:45.2247856Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:45.2248012Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:57:45.2248087Z         
2025-03-14T04:57:45.2248399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.2248531Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2248623Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:57:45.2248760Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:57:45.2248826Z         
2025-03-14T04:57:45.2249141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.2249288Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:45.2249390Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:57:45.2249524Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:57:45.2249603Z         
2025-03-14T04:57:45.2249986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.2250158Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.2250275Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:57:45.2250367Z         
2025-03-14T04:57:45.2250669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.2250833Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.2250947Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:57:45.2251047Z         
2025-03-14T04:57:45.2251349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.2251510Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.2251626Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:57:45.2251707Z         
2025-03-14T04:57:45.2252012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.2252209Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:45.2252325Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:57:45.2252403Z         
2025-03-14T04:57:45.2252740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.2252897Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:45.2252966Z         
2025-03-14T04:57:45.2253307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.2253450Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:45.2253528Z         
2025-03-14T04:57:45.2253922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.2254070Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:45.2254209Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:57:45.2254364Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:45.2254512Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:57:45.2254578Z         
2025-03-14T04:57:45.2254929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.2255074Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:45.2255206Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:57:45.2255359Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:45.2255502Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:57:45.2255568Z         
2025-03-14T04:57:45.2255921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.2256044Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:45.2256214Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:45.2256363Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:57:45.2256435Z         
2025-03-14T04:57:45.2256766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.2256891Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:45.2257075Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:45.2257220Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:57:45.2257286Z         
2025-03-14T04:57:45.2257600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.2257699Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:45.2257826Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:45.2257893Z         
2025-03-14T04:57:45.2258205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.2258301Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:45.2258421Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:45.2258489Z         
2025-03-14T04:57:45.2258797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.2258912Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:45.2259052Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:45.2259119Z         
2025-03-14T04:57:45.2259448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.2259564Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:45.2259697Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:45.2259762Z         
2025-03-14T04:57:45.2260109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.2260293Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:45.2260366Z         
2025-03-14T04:57:45.2260700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.2260872Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:57:45.2260938Z         
2025-03-14T04:57:45.2261324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.2261500Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:45.2261575Z         
2025-03-14T04:57:45.2261997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.2262220Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:57:45.2262305Z         
2025-03-14T04:57:45.2262752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.2262917Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:57:45.2263074Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:57:45.2263243Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:57:45.2263313Z         
2025-03-14T04:57:45.2263700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.2263875Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:57:45.2263952Z         
2025-03-14T04:57:45.2264371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.2264535Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:57:45.2264602Z         
2025-03-14T04:57:45.2264936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.2265084Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:57:45.2265229Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2265388Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:57:45.2265472Z         
2025-03-14T04:57:45.2265806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.2265977Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:57:45.2266118Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:57:45.2266283Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:57:45.2266353Z         
2025-03-14T04:57:45.2266677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.2266804Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2266910Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:57:45.2267049Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:57:45.2267125Z         
2025-03-14T04:57:45.2267442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.2267606Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:57:45.2267704Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:57:45.2267849Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:57:45.2267916Z         
2025-03-14T04:57:45.2268253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.2268416Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.2268561Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:57:45.2268630Z         
2025-03-14T04:57:45.2268948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.2269104Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.2269229Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:57:45.2269312Z         
2025-03-14T04:57:45.2269623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.2269783Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.2269898Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:57:45.2269974Z         
2025-03-14T04:57:45.2270278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.2270477Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:57:45.2270593Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:57:45.2270665Z         
2025-03-14T04:57:45.2271003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.2271158Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:57:45.2271224Z         
2025-03-14T04:57:45.2271566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.2271711Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:57:45.2271785Z         
2025-03-14T04:57:45.2272152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.2272303Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.2272440Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:57:45.2272623Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:57:45.2272768Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:57:45.2272847Z         
2025-03-14T04:57:45.2273202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.2273358Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.2273488Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:57:45.2273656Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:57:45.2273803Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:57:45.2273881Z         
2025-03-14T04:57:45.2274242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.2274375Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:57:45.2274556Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:57:45.2274708Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:57:45.2274775Z         
2025-03-14T04:57:45.2275116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.2275235Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:57:45.2275433Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:57:45.2275572Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:57:45.2275648Z         
2025-03-14T04:57:45.2275968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.2276078Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:57:45.2276211Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:57:45.2276279Z         
2025-03-14T04:57:45.2276593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.2276696Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:57:45.2276824Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:57:45.2276890Z         
2025-03-14T04:57:45.2277208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.2277328Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:57:45.2277476Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:57:45.2277542Z         
2025-03-14T04:57:45.2277874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.2277995Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:57:45.2278136Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:57:45.2278204Z         
2025-03-14T04:57:45.2278565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.2278767Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:57:45.2278843Z         
2025-03-14T04:57:45.2279182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.2279360Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:57:45.2279429Z         
2025-03-14T04:57:45.2279828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.2280028Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:57:45.2280102Z         
2025-03-14T04:57:45.2280512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.2280750Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:57:45.2280817Z         
2025-03-14T04:57:45.2281273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.2281636Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:57:45.2281872Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:57:45.2282015Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:57:45.2282094Z         
2025-03-14T04:57:45.2282477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.2282666Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:57:45.2282754Z         
2025-03-14T04:57:45.2283086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.2283237Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:57:45.2283317Z         
2025-03-14T04:57:45.2283637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.2283782Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:57:45.2283911Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2284076Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:57:45.2284147Z         
2025-03-14T04:57:45.2284507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.2284645Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:57:45.2284768Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:57:45.2284930Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:57:45.2284997Z         
2025-03-14T04:57:45.2285315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.2285439Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2285553Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:57:45.2285685Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:57:45.2285760Z         
2025-03-14T04:57:45.2286067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.2286238Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:57:45.2286334Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:57:45.2286493Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:57:45.2286559Z         
2025-03-14T04:57:45.2286872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.2287026Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.2287172Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:57:45.2287238Z         
2025-03-14T04:57:45.2287543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.2287696Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.2287834Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:57:45.2287898Z         
2025-03-14T04:57:45.2288201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.2288350Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.2288472Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:57:45.2288538Z         
2025-03-14T04:57:45.2288848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.2289031Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:57:45.2289150Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:57:45.2289216Z         
2025-03-14T04:57:45.2289556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.2289698Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:57:45.2289773Z         
2025-03-14T04:57:45.2290102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.2290261Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:57:45.2290328Z         
2025-03-14T04:57:45.2290677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.2290819Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.2290945Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:57:45.2291102Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:57:45.2291241Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:57:45.2291317Z         
2025-03-14T04:57:45.2291666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.2291809Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.2291931Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:57:45.2292089Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:57:45.2292251Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:57:45.2292325Z         
2025-03-14T04:57:45.2292648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.2292795Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:57:45.2292954Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:57:45.2293096Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:57:45.2293162Z         
2025-03-14T04:57:45.2293496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.2293627Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:57:45.2293803Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:57:45.2293937Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:57:45.2294008Z         
2025-03-14T04:57:45.2294319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.2294423Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:57:45.2294540Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:57:45.2294611Z         
2025-03-14T04:57:45.2294920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.2295024Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:57:45.2295144Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:57:45.2295216Z         
2025-03-14T04:57:45.2295522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.2295649Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:57:45.2295783Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:57:45.2295872Z         
2025-03-14T04:57:45.2296175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.2296298Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:57:45.2296430Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:57:45.2296502Z         
2025-03-14T04:57:45.2296844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.2297043Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:57:45.2297108Z         
2025-03-14T04:57:45.2297447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.2297607Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:57:45.2297680Z         
2025-03-14T04:57:45.2298057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.2298264Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:57:45.2298341Z         
2025-03-14T04:57:45.2323922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.2324529Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:57:45.2324611Z         
2025-03-14T04:57:45.2325108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.2325305Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:57:45.2325473Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:57:45.2325628Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:57:45.2325706Z         
2025-03-14T04:57:45.2326111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.2326342Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:57:45.2326427Z         
2025-03-14T04:57:45.2326752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.2326911Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:57:45.2326982Z         
2025-03-14T04:57:45.2327311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.2327448Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:57:45.2327583Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2327735Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:57:45.2327809Z         
2025-03-14T04:57:45.2328169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.2328305Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:57:45.2328427Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:57:45.2328584Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:57:45.2328648Z         
2025-03-14T04:57:45.2328965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.2329088Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2329191Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:57:45.2329327Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:57:45.2329405Z         
2025-03-14T04:57:45.2329715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.2329875Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:57:45.2329973Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:57:45.2330137Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:57:45.2330204Z         
2025-03-14T04:57:45.2330544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.2330745Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.2330865Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:57:45.2330942Z         
2025-03-14T04:57:45.2331252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.2331422Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.2331556Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:57:45.2331631Z         
2025-03-14T04:57:45.2331931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.2332089Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.2332201Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:57:45.2332276Z         
2025-03-14T04:57:45.2332581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.2332776Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:57:45.2332888Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:57:45.2332963Z         
2025-03-14T04:57:45.2333299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.2333449Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:57:45.2333516Z         
2025-03-14T04:57:45.2333875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.2334014Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:57:45.2334090Z         
2025-03-14T04:57:45.2334437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.2334585Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.2334711Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:57:45.2334873Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:57:45.2335015Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:57:45.2335090Z         
2025-03-14T04:57:45.2335436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.2335579Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.2335702Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:57:45.2335864Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:57:45.2336020Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:57:45.2336093Z         
2025-03-14T04:57:45.2336429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.2336569Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:57:45.2336737Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:57:45.2336873Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:57:45.2336948Z         
2025-03-14T04:57:45.2337275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.2337458Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:57:45.2337626Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:57:45.2337765Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:57:45.2337835Z         
2025-03-14T04:57:45.2338155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.2338261Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:57:45.2338394Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:57:45.2338465Z         
2025-03-14T04:57:45.2338781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.2338881Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:57:45.2339010Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:57:45.2339078Z         
2025-03-14T04:57:45.2339389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.2339509Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:57:45.2339673Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:57:45.2339739Z         
2025-03-14T04:57:45.2340047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.2340160Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:57:45.2340300Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:57:45.2340366Z         
2025-03-14T04:57:45.2340718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.2340911Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:57:45.2340988Z         
2025-03-14T04:57:45.2341327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.2341500Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:57:45.2341566Z         
2025-03-14T04:57:45.2341979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.2342157Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:57:45.2342232Z         
2025-03-14T04:57:45.2342640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.2342876Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:57:45.2342943Z         
2025-03-14T04:57:45.2343392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.2343562Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:57:45.2343729Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:57:45.2343867Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:57:45.2343953Z         
2025-03-14T04:57:45.2344495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.2344678Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:57:45.2344758Z         
2025-03-14T04:57:45.2345086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.2345257Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:57:45.2345328Z         
2025-03-14T04:57:45.2345654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.2345788Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:57:45.2345932Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2346102Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:57:45.2346183Z         
2025-03-14T04:57:45.2346508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.2346640Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:57:45.2346765Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:57:45.2346927Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:57:45.2346994Z         
2025-03-14T04:57:45.2347317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.2347445Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.2347548Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:57:45.2347683Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:57:45.2347756Z         
2025-03-14T04:57:45.2348070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.2348230Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:57:45.2348341Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:57:45.2348480Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:57:45.2348547Z         
2025-03-14T04:57:45.2348863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.2349038Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.2349164Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:57:45.2349233Z         
2025-03-14T04:57:45.2349544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.2349716Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.2349843Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:57:45.2349910Z         
2025-03-14T04:57:45.2350219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.2350370Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.2350493Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:57:45.2350561Z         
2025-03-14T04:57:45.2350877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.2351072Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:57:45.2351185Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:57:45.2351260Z         
2025-03-14T04:57:45.2351600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.2351751Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:57:45.2351821Z         
2025-03-14T04:57:45.2352180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.2352318Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:57:45.2352391Z         
2025-03-14T04:57:45.2352745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.2352904Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.2353031Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:57:45.2353192Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:57:45.2353334Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:57:45.2353407Z         
2025-03-14T04:57:45.2353765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.2353914Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.2354037Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:57:45.2354225Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:57:45.2354365Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:57:45.2354440Z         
2025-03-14T04:57:45.2354774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.2354917Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:57:45.2355084Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:57:45.2355227Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:57:45.2355293Z         
2025-03-14T04:57:45.2355639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.2355776Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:57:45.2355957Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:57:45.2356093Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:57:45.2356168Z         
2025-03-14T04:57:45.2356485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.2356592Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:57:45.2356711Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:57:45.2356785Z         
2025-03-14T04:57:45.2357097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.2357207Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:57:45.2357324Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:57:45.2357398Z         
2025-03-14T04:57:45.2357714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.2357839Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:57:45.2357990Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:57:45.2358065Z         
2025-03-14T04:57:45.2358362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.2358483Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:57:45.2358619Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:57:45.2358683Z         
2025-03-14T04:57:45.2359026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.2359211Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:57:45.2359283Z         
2025-03-14T04:57:45.2359611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.2359776Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:57:45.2359841Z         
2025-03-14T04:57:45.2360239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.2360415Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:57:45.2360486Z         
2025-03-14T04:57:45.2360965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:45.2361135Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:45.2361200Z         
2025-03-14T04:57:45.2361504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2361660Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:57:45.2361732Z         
2025-03-14T04:57:45.2362166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.2362288Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:57:45.2362394Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:57:45.2362516Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:45.2362583Z         
2025-03-14T04:57:45.2363045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.2363176Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.2363417Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:57:45.2363482Z         
2025-03-14T04:57:45.2363941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.2364110Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.2364201Z         
2025-03-14T04:57:45.2364495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2364624Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:57:45.2364694Z         
2025-03-14T04:57:45.2365127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.2365254Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:57:45.2365364Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:57:45.2365492Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:57:45.2365557Z         
2025-03-14T04:57:45.2366018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.2366150Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.2366388Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:57:45.2366470Z         
2025-03-14T04:57:45.2366932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.2367118Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.2367191Z         
2025-03-14T04:57:45.2367484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2367620Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:57:45.2367684Z         
2025-03-14T04:57:45.2368120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.2368254Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:57:45.2368366Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:57:45.2368482Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:57:45.2368556Z         
2025-03-14T04:57:45.2369010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.2369151Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.2369386Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:57:45.2369462Z         
2025-03-14T04:57:45.2369914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.2370086Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.2370153Z         
2025-03-14T04:57:45.2370469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2370593Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:57:45.2370667Z         
2025-03-14T04:57:45.2371090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.2371212Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:57:45.2371325Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:57:45.2371439Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:57:45.2371512Z         
2025-03-14T04:57:45.2371960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.2372101Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.2372331Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:57:45.2372407Z         
2025-03-14T04:57:45.2372865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.2373040Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.2373123Z         
2025-03-14T04:57:45.2373422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2373545Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:57:45.2373619Z         
2025-03-14T04:57:45.2374037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.2374206Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:57:45.2374310Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:57:45.2374433Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:57:45.2374497Z         
2025-03-14T04:57:45.2374953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.2375121Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:45.2375360Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:57:45.2375426Z         
2025-03-14T04:57:45.2375877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.2376041Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.2376116Z         
2025-03-14T04:57:45.2376405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.2376536Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:57:45.2376624Z         
2025-03-14T04:57:45.2376913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.2377297Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:57:45.2377365Z         
2025-03-14T04:57:45.2377651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.2378114Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:57:45.2378190Z         
2025-03-14T04:57:45.2378465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.2378674Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:57:45.2378741Z         
2025-03-14T04:57:45.2379150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:45.2379292Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:57:45.2379383Z         
2025-03-14T04:57:45.2379676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.2379837Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:57:45.2379902Z         
2025-03-14T04:57:45.2380289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:45.2380441Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:57:45.2380516Z         
2025-03-14T04:57:45.2380995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:45.2381142Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:57:45.2381263Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:45.2381629Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:45.2381773Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:45.2381850Z         
2025-03-14T04:57:45.2382213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:45.2382344Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:45.2382412Z         
2025-03-14T04:57:45.2382859Z 
2025-03-14T04:57:45.2382956Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:45.2516713Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: 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L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:57:45.2671861Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:45.2672627Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2673730Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2675143Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2676194Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2677125Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2678059Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2679036Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2680136Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2681190Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2682317Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2683316Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2684270Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2685485Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2686649Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2687572Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2688455Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2689359Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2690297Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2691210Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2692146Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2693021Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.2693943Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2694922Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2695869Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.2696785Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.2697670Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2698548Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2699508Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2700423Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2701329Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2702179Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2703070Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2704022Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2705041Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2705945Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2706812Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2707689Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2708629Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2709541Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2710424Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2711265Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2712154Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2713095Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2714014Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2714885Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2715726Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2716623Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2717570Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2718482Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2719358Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2720199Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2721084Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2722026Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2722930Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2723806Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2724648Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2725521Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2726444Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2727396Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2728275Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2729145Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2730042Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2731001Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2731901Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2732790Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2733640Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2734521Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2735454Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2736378Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2737265Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2738141Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.2739048Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2740014Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2740980Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.2741967Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.2742849Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2743779Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2744935Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2745896Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2746831Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2747709Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2748623Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2749594Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2750536Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2751459Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2752356Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2753272Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2754241Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2755201Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2756125Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2757007Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2757914Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2758848Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2759774Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2760658Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2761527Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2762407Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2763349Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2764261Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2765149Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2765998Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2766896Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2767864Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2768780Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2769675Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2770530Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2771424Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2772338Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2773245Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2774109Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2774905Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2775721Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2776601Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2777445Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2778255Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2779035Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2779852Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2780714Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2781759Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2782650Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2783535Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2784448Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2785438Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2786388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2787331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2788224Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2789150Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2790096Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2793985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2794801Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2795575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2796384Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2797259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2798111Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2798924Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2799751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.2800597Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2801490Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2802368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.2803191Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.2803966Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2804798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2805659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2806515Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2807320Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2808139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2808950Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2809827Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2810665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2811475Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2812254Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2813077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2813938Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2814772Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2815583Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2816363Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2817170Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2818046Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2818877Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2819718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2820526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2821341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2822218Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2823063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2823882Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2824839Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2825701Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2826567Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2827479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2828345Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2829176Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2830018Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2830884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2831730Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2832572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2833364Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2834200Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2835062Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2835918Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2836738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2837518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2838328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2839189Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2840026Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2840857Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2841635Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2842451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2843318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2844164Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2844978Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2845753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2846584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2847451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2848317Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2849130Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2849946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2850766Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2851640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2852476Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2853289Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2854072Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2854904Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2855780Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2856610Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2857416Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2858198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2859001Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2859876Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2860712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2861537Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2862314Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2863137Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2864021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2865027Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2865880Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2866749Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2867661Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2868641Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2869612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2870526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2871400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2872267Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2873128Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2873966Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2874794Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2875576Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2876400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2877264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2878122Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2878932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2879718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2880528Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2881390Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2882355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2883242Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2884019Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2884808Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2885658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2886476Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2887269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2888058Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2888854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2889723Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2890542Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2891341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2892121Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2892914Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2893758Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2894573Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2895361Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2896116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2896916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2897757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2898584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2899384Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2900158Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2900953Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2901829Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2902659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2903479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2904449Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2905468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2906495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2907560Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2908591Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2909595Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2910619Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2911687Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2912526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2913338Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2914114Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2914921Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2915786Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2916630Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2917498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2918280Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2919114Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2919980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2920836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2921657Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2922435Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2923249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2924116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2924963Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2925798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2926577Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2927392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2928249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2929089Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2929897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2930692Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2931502Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2932388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2933224Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2934057Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2934838Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2935654Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2936519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2937357Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2938181Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2938981Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2939796Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2940665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2941511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2942336Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2943118Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2943962Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2944957Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2945865Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2946729Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2947558Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2948453Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2949371Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2950265Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2951132Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2951958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2952819Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2953712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2954543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2955341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2956100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2956893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2957734Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2958588Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2959382Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2960160Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2960954Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2961818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2962646Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2963458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2964239Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2965052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2965917Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2966775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2967591Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2968380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2969193Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2970065Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2970913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2971745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2972536Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2973357Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2974237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2975090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2975922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2976706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2977522Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2978385Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2979235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2980052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2980855Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2981893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2982775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2983626Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2984615Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2985467Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2986351Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2987265Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2988180Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.2989044Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.2989899Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:57:45.2990758Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2991673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2992568Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.2993433Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.2994261Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:57:45.2995633Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.2996508Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.2997363Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.2998180Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.2998965Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:57:45.2999777Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3000656Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3001507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3002341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3003126Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3003941Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3004821Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3005660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3006473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3007254Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3008076Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3008949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3009821Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3010652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3011483Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3012303Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3013172Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3014019Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3014854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3015643Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3016515Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3017383Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3018248Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3019070Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3019867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3020686Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3021566Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3022411Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3023246Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3024073Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3025080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3026013Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3026893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3027783Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3028643Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3029507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3030440Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3031328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3032199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3033045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3033913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3034831Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3035717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3036581Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3037382Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3038214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3039078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3039919Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3040732Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3041510Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3042315Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3043197Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3044049Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3044883Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3045667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3046501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3047373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3048223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3049034Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3049823Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3050636Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3051516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3052355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3053167Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3053951Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3054750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3055621Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3056474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3057288Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3058092Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3058908Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3059775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3060649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3061468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3062256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3063073Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3063939Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3064896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3065793Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3066621Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3067495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3068430Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3069343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3070198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3071038Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3071905Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3072841Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3073730Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3074601Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3075389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3076201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3077069Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3077920Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3078750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3079553Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3080362Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3081227Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3082249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3083074Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3083852Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3084714Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3085587Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3086454Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3087265Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3088053Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3088894Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3089764Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3090608Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3091420Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3092193Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3092998Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3093881Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3094724Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3095536Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3096334Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.3097175Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3098062Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3098989Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.3099836Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.3100648Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3101465Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3102343Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3103181Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3104005Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3104878Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3105736Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3106627Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3107484Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3108294Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3108655Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3109065Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3109476Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3109859Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3110257Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3110616Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3111048Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3111462Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3111848Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3112253Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3112605Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3113024Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3113433Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3113823Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3114207Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3114577Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3114988Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3115395Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3115785Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3116171Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3116405Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:57:45.3116642Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:57:45.3116892Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:57:45.3117115Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:57:45.3117350Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:57:45.3117572Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:57:45.3117787Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:57:45.3118002Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:57:45.3118240Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:57:45.3118459Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:57:45.3118676Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:57:45.3118893Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:57:45.3119112Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:57:45.3119333Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:57:45.3119549Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:57:45.3119767Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:57:45.3120128Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:45.3120477Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:57:45.3120903Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:57:45.3121248Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:57:45.3121600Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:57:45.3121921Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:45.3122243Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:45.3122609Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:45.3122976Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:45.3123352Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:45.3123692Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:45.3123789Z         
2025-03-14T04:57:45.3124104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3124662Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3124748Z         
2025-03-14T04:57:45.3125046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3126803Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3126876Z         
2025-03-14T04:57:45.3127177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:45.3127325Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:45.3127400Z         
2025-03-14T04:57:45.3127779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:45.3128027Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:45.3128094Z         
2025-03-14T04:57:45.3128359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3128849Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3128923Z         
2025-03-14T04:57:45.3129193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3131011Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3131106Z         
2025-03-14T04:57:45.3131401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3131566Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:45.3131632Z         
2025-03-14T04:57:45.3131897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3132410Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3132481Z         
2025-03-14T04:57:45.3132755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3134561Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3134639Z         
2025-03-14T04:57:45.3134943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3135086Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:45.3135158Z         
2025-03-14T04:57:45.3135411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3135945Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3136011Z         
2025-03-14T04:57:45.3136289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3138073Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3138205Z         
2025-03-14T04:57:45.3138482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3138987Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.3139064Z         
2025-03-14T04:57:45.3139331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3141218Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3141297Z         
2025-03-14T04:57:45.3141574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3141728Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:45.3141794Z         
2025-03-14T04:57:45.3142084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3142236Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:45.3142312Z         
2025-03-14T04:57:45.3142562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3143053Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3143119Z         
2025-03-14T04:57:45.3143408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3145324Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3145431Z         
2025-03-14T04:57:45.3145749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3145903Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:45.3145986Z         
2025-03-14T04:57:45.3146256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3146798Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3146879Z         
2025-03-14T04:57:45.3147174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3149084Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3149164Z         
2025-03-14T04:57:45.3149477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3149635Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:45.3149705Z         
2025-03-14T04:57:45.3149975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3150529Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3150608Z         
2025-03-14T04:57:45.3150895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3152803Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3152925Z         
2025-03-14T04:57:45.3153232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3153408Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:45.3153476Z         
2025-03-14T04:57:45.3153789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3153946Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:45.3154024Z         
2025-03-14T04:57:45.3154295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3154811Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3154881Z         
2025-03-14T04:57:45.3155187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3157059Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3157131Z         
2025-03-14T04:57:45.3157437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3157577Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:45.3157665Z         
2025-03-14T04:57:45.3157918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3158413Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3158495Z         
2025-03-14T04:57:45.3158770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3160567Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3160648Z         
2025-03-14T04:57:45.3160944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3161086Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:45.3161161Z         
2025-03-14T04:57:45.3161408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3161933Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3162008Z         
2025-03-14T04:57:45.3162275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3164057Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3164131Z         
2025-03-14T04:57:45.3164424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3164588Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:45.3164653Z         
2025-03-14T04:57:45.3164942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3165110Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:45.3165180Z         
2025-03-14T04:57:45.3165431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3165928Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3166007Z         
2025-03-14T04:57:45.3166278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3168053Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3168130Z         
2025-03-14T04:57:45.3168420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3168579Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:45.3168652Z         
2025-03-14T04:57:45.3168904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3169406Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3169474Z         
2025-03-14T04:57:45.3169750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3171528Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3171610Z         
2025-03-14T04:57:45.3171902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3172046Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:45.3172120Z         
2025-03-14T04:57:45.3172370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3172889Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3172957Z         
2025-03-14T04:57:45.3173226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3175010Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3175092Z         
2025-03-14T04:57:45.3175357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3175855Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.3175930Z         
2025-03-14T04:57:45.3176203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3178070Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3178163Z         
2025-03-14T04:57:45.3178447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3178596Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:45.3178670Z         
2025-03-14T04:57:45.3178947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3179127Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:45.3179197Z         
2025-03-14T04:57:45.3179468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3179969Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3180044Z         
2025-03-14T04:57:45.3180303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3182454Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3182548Z         
2025-03-14T04:57:45.3182869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3183043Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:45.3183117Z         
2025-03-14T04:57:45.3183407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3183970Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3184051Z         
2025-03-14T04:57:45.3184396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3186399Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3186534Z         
2025-03-14T04:57:45.3186868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3187037Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:45.3187111Z         
2025-03-14T04:57:45.3187400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3187955Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3188036Z         
2025-03-14T04:57:45.3188387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3190366Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3190452Z         
2025-03-14T04:57:45.3190778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3190961Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:45.3191045Z         
2025-03-14T04:57:45.3191368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3191534Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:45.3191615Z         
2025-03-14T04:57:45.3191897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3192455Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3192533Z         
2025-03-14T04:57:45.3192830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3194679Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3194768Z         
2025-03-14T04:57:45.3195055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3195204Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:45.3195269Z         
2025-03-14T04:57:45.3195528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3196024Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3196101Z         
2025-03-14T04:57:45.3196368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3198163Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3198241Z         
2025-03-14T04:57:45.3198521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3198668Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:45.3198735Z         
2025-03-14T04:57:45.3199000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3199488Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3199576Z         
2025-03-14T04:57:45.3199839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3201609Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3201700Z         
2025-03-14T04:57:45.3201980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3202141Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:45.3202209Z         
2025-03-14T04:57:45.3202500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3202649Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:45.3202722Z         
2025-03-14T04:57:45.3202970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3203478Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3203546Z         
2025-03-14T04:57:45.3203818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3205609Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3205679Z         
2025-03-14T04:57:45.3205974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3206114Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:45.3206199Z         
2025-03-14T04:57:45.3206447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3206945Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3207035Z         
2025-03-14T04:57:45.3207301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3209062Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3209138Z         
2025-03-14T04:57:45.3209423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3209573Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:45.3209638Z         
2025-03-14T04:57:45.3209910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3210399Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3210472Z         
2025-03-14T04:57:45.3210734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3212519Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3212596Z         
2025-03-14T04:57:45.3212899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3213063Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:45.3213129Z         
2025-03-14T04:57:45.3213425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3213571Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:45.3213655Z         
2025-03-14T04:57:45.3213913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3214389Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3214453Z         
2025-03-14T04:57:45.3214721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3216452Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3216518Z         
2025-03-14T04:57:45.3216807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3216947Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:45.3217020Z         
2025-03-14T04:57:45.3217269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3217765Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3217831Z         
2025-03-14T04:57:45.3218105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3219891Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3219973Z         
2025-03-14T04:57:45.3220283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3220422Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:45.3220495Z         
2025-03-14T04:57:45.3220744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3221245Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3221317Z         
2025-03-14T04:57:45.3221581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3223371Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3223445Z         
2025-03-14T04:57:45.3223694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3224268Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.3224349Z         
2025-03-14T04:57:45.3224636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3226536Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3226622Z         
2025-03-14T04:57:45.3226909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3227065Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:45.3227140Z         
2025-03-14T04:57:45.3227421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3227573Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:45.3227639Z         
2025-03-14T04:57:45.3227898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3228374Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3228447Z         
2025-03-14T04:57:45.3228713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3230546Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3230622Z         
2025-03-14T04:57:45.3230907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3231055Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:45.3231121Z         
2025-03-14T04:57:45.3231379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3231862Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3231934Z         
2025-03-14T04:57:45.3232211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3233984Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3234088Z         
2025-03-14T04:57:45.3234375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3234517Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:45.3234583Z         
2025-03-14T04:57:45.3234839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3235337Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3235405Z         
2025-03-14T04:57:45.3235675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3237473Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3237550Z         
2025-03-14T04:57:45.3237836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3237985Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:45.3238060Z         
2025-03-14T04:57:45.3238337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3238485Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:45.3238551Z         
2025-03-14T04:57:45.3238828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3239306Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3239396Z         
2025-03-14T04:57:45.3239661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3241431Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3241522Z         
2025-03-14T04:57:45.3241814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3241959Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:45.3242023Z         
2025-03-14T04:57:45.3242284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3242779Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3242877Z         
2025-03-14T04:57:45.3243139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3244902Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3244977Z         
2025-03-14T04:57:45.3245259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3245411Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:45.3245477Z         
2025-03-14T04:57:45.3245734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3246262Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3246338Z         
2025-03-14T04:57:45.3246598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3248370Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3248447Z         
2025-03-14T04:57:45.3248726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3248880Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:45.3248946Z         
2025-03-14T04:57:45.3249235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3249393Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:45.3249467Z         
2025-03-14T04:57:45.3249711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3250191Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3250267Z         
2025-03-14T04:57:45.3250529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3252308Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3252399Z         
2025-03-14T04:57:45.3252683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3252824Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:45.3252891Z         
2025-03-14T04:57:45.3253142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3253637Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3253711Z         
2025-03-14T04:57:45.3253975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3255787Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3255863Z         
2025-03-14T04:57:45.3256171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3256312Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:45.3256377Z         
2025-03-14T04:57:45.3256632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3257121Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3257196Z         
2025-03-14T04:57:45.3257456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3259265Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3259357Z         
2025-03-14T04:57:45.3259638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3259791Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:45.3259869Z         
2025-03-14T04:57:45.3260164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3260305Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:45.3260380Z         
2025-03-14T04:57:45.3260630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3261119Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3261184Z         
2025-03-14T04:57:45.3261460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3263277Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3263348Z         
2025-03-14T04:57:45.3263646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3263782Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:45.3263858Z         
2025-03-14T04:57:45.3264163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3264665Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3264738Z         
2025-03-14T04:57:45.3265004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3266846Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3266956Z         
2025-03-14T04:57:45.3267261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3267417Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:45.3267483Z         
2025-03-14T04:57:45.3267740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3268224Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3268300Z         
2025-03-14T04:57:45.3268566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3270343Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3270425Z         
2025-03-14T04:57:45.3270704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3270857Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:45.3270922Z         
2025-03-14T04:57:45.3271213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3271352Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:45.3271426Z         
2025-03-14T04:57:45.3271675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3272168Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3272246Z         
2025-03-14T04:57:45.3272517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3274288Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3274372Z         
2025-03-14T04:57:45.3274669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3274805Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:45.3274882Z         
2025-03-14T04:57:45.3275134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3275627Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3275695Z         
2025-03-14T04:57:45.3275987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3277743Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3277812Z         
2025-03-14T04:57:45.3278102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3278236Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:45.3278308Z         
2025-03-14T04:57:45.3278569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3279064Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3279151Z         
2025-03-14T04:57:45.3279418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3281206Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3281295Z         
2025-03-14T04:57:45.3281735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3281902Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:45.3281969Z         
2025-03-14T04:57:45.3282264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3282406Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:45.3282484Z         
2025-03-14T04:57:45.3282777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3283257Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3283326Z         
2025-03-14T04:57:45.3283600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3285377Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3285455Z         
2025-03-14T04:57:45.3285750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3285906Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:57:45.3285983Z         
2025-03-14T04:57:45.3286235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3286728Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3286824Z         
2025-03-14T04:57:45.3287094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3288880Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3288949Z         
2025-03-14T04:57:45.3289247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3289395Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:57:45.3289469Z         
2025-03-14T04:57:45.3289716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3290216Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3290285Z         
2025-03-14T04:57:45.3290554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3292365Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3292449Z         
2025-03-14T04:57:45.3292740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3292886Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:57:45.3292961Z         
2025-03-14T04:57:45.3293243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3293406Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:57:45.3293472Z         
2025-03-14T04:57:45.3293738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3294213Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3294280Z         
2025-03-14T04:57:45.3294543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3296274Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3296352Z         
2025-03-14T04:57:45.3296636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3296770Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:57:45.3296842Z         
2025-03-14T04:57:45.3297086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3297564Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3297629Z         
2025-03-14T04:57:45.3297894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3299622Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3299727Z         
2025-03-14T04:57:45.3300015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3300148Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:57:45.3300221Z         
2025-03-14T04:57:45.3300473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3300964Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3301029Z         
2025-03-14T04:57:45.3301301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3303081Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3303151Z         
2025-03-14T04:57:45.3303437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3303582Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:57:45.3303657Z         
2025-03-14T04:57:45.3303936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3304133Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:57:45.3304210Z         
2025-03-14T04:57:45.3304480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3304999Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3305080Z         
2025-03-14T04:57:45.3305356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3307240Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3307332Z         
2025-03-14T04:57:45.3307620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3307770Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:57:45.3307836Z         
2025-03-14T04:57:45.3308091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3308588Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3308655Z         
2025-03-14T04:57:45.3308926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3310710Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3310788Z         
2025-03-14T04:57:45.3311080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3311218Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:57:45.3311289Z         
2025-03-14T04:57:45.3311538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3312046Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3312134Z         
2025-03-14T04:57:45.3312409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3314202Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3314519Z         
2025-03-14T04:57:45.3314816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3314970Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:57:45.3315048Z         
2025-03-14T04:57:45.3315337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3315486Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:57:45.3315552Z         
2025-03-14T04:57:45.3315815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3316312Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3316386Z         
2025-03-14T04:57:45.3316647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3318419Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3318509Z         
2025-03-14T04:57:45.3318795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3318939Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:57:45.3319020Z         
2025-03-14T04:57:45.3319276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3319753Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3319839Z         
2025-03-14T04:57:45.3320101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3321878Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3321953Z         
2025-03-14T04:57:45.3322238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3322385Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:57:45.3322451Z         
2025-03-14T04:57:45.3322724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3323213Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3323279Z         
2025-03-14T04:57:45.3323551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3325332Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3325410Z         
2025-03-14T04:57:45.3325695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3325861Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:57:45.3325936Z         
2025-03-14T04:57:45.3326221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3326369Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:57:45.3326451Z         
2025-03-14T04:57:45.3326710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3327197Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3327272Z         
2025-03-14T04:57:45.3327536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3329347Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3329427Z         
2025-03-14T04:57:45.3329713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3329860Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:57:45.3329926Z         
2025-03-14T04:57:45.3330182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3330668Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3330743Z         
2025-03-14T04:57:45.3331009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3332851Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3332942Z         
2025-03-14T04:57:45.3333240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3333404Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:57:45.3333469Z         
2025-03-14T04:57:45.3333729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3334231Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3334318Z         
2025-03-14T04:57:45.3334580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3336350Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3336425Z         
2025-03-14T04:57:45.3336700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3336852Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:57:45.3336916Z         
2025-03-14T04:57:45.3337199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3337337Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:57:45.3337412Z         
2025-03-14T04:57:45.3337653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3338154Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3338228Z         
2025-03-14T04:57:45.3338486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3340284Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3340374Z         
2025-03-14T04:57:45.3340658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3340804Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:57:45.3340869Z         
2025-03-14T04:57:45.3341129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3341614Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3341687Z         
2025-03-14T04:57:45.3341947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3343759Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3343839Z         
2025-03-14T04:57:45.3344198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3344360Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:57:45.3344427Z         
2025-03-14T04:57:45.3344698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3345239Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3345333Z         
2025-03-14T04:57:45.3345615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3347394Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3347488Z         
2025-03-14T04:57:45.3347771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3347932Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:57:45.3347999Z         
2025-03-14T04:57:45.3348297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3348438Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:57:45.3348514Z         
2025-03-14T04:57:45.3348766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3349268Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3349343Z         
2025-03-14T04:57:45.3349608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3351443Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3351509Z         
2025-03-14T04:57:45.3351855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3352000Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:57:45.3352080Z         
2025-03-14T04:57:45.3352329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3352827Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3352918Z         
2025-03-14T04:57:45.3353188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3354929Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3355004Z         
2025-03-14T04:57:45.3355281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3355424Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:57:45.3355487Z         
2025-03-14T04:57:45.3355754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3356226Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3356303Z         
2025-03-14T04:57:45.3356559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3358319Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3358395Z         
2025-03-14T04:57:45.3358667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3358837Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:57:45.3358902Z         
2025-03-14T04:57:45.3359183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3359320Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:57:45.3359405Z         
2025-03-14T04:57:45.3359651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3360130Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3360196Z         
2025-03-14T04:57:45.3360467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3362270Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3362339Z         
2025-03-14T04:57:45.3362635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3362773Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:57:45.3362847Z         
2025-03-14T04:57:45.3363098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3363594Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3363668Z         
2025-03-14T04:57:45.3363932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3365742Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3365825Z         
2025-03-14T04:57:45.3366114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3366271Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:57:45.3366337Z         
2025-03-14T04:57:45.3366593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3367081Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3367157Z         
2025-03-14T04:57:45.3367419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3369216Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3369297Z         
2025-03-14T04:57:45.3369579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3369734Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:57:45.3369800Z         
2025-03-14T04:57:45.3370091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3370235Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:57:45.3370310Z         
2025-03-14T04:57:45.3370558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3371059Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3371127Z         
2025-03-14T04:57:45.3371394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3373190Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3373287Z         
2025-03-14T04:57:45.3373578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3373715Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:57:45.3373789Z         
2025-03-14T04:57:45.3374036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3374522Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3374590Z         
2025-03-14T04:57:45.3374861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3376642Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3376710Z         
2025-03-14T04:57:45.3377006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3377141Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:57:45.3377214Z         
2025-03-14T04:57:45.3377461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3377970Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3378043Z         
2025-03-14T04:57:45.3378318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3380106Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3380195Z         
2025-03-14T04:57:45.3380476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3380633Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:57:45.3380698Z         
2025-03-14T04:57:45.3380986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3381126Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:57:45.3381197Z         
2025-03-14T04:57:45.3381683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3382235Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3382305Z         
2025-03-14T04:57:45.3382594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3384542Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3384629Z         
2025-03-14T04:57:45.3384963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3385108Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:57:45.3385187Z         
2025-03-14T04:57:45.3385474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3385994Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3386064Z         
2025-03-14T04:57:45.3386382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3388259Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3388333Z         
2025-03-14T04:57:45.3388643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3388786Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:57:45.3388864Z         
2025-03-14T04:57:45.3389142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3389667Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3389740Z         
2025-03-14T04:57:45.3390029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3391911Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3391984Z         
2025-03-14T04:57:45.3392287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3392458Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:57:45.3392535Z         
2025-03-14T04:57:45.3392838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3392996Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:57:45.3393080Z         
2025-03-14T04:57:45.3393354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3393862Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3393933Z         
2025-03-14T04:57:45.3394214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3396005Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3396083Z         
2025-03-14T04:57:45.3396371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3396505Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:57:45.3396582Z         
2025-03-14T04:57:45.3396829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3397319Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3397385Z         
2025-03-14T04:57:45.3397654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3399435Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3399525Z         
2025-03-14T04:57:45.3399824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3399975Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:57:45.3400048Z         
2025-03-14T04:57:45.3400298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3400789Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3400857Z         
2025-03-14T04:57:45.3401129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3402932Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3403002Z         
2025-03-14T04:57:45.3403297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3403447Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:57:45.3403522Z         
2025-03-14T04:57:45.3403806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3403957Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:57:45.3404022Z         
2025-03-14T04:57:45.3404277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3404770Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3404846Z         
2025-03-14T04:57:45.3405116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3406879Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3406982Z         
2025-03-14T04:57:45.3407271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3407417Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:57:45.3407481Z         
2025-03-14T04:57:45.3407739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3408235Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3408304Z         
2025-03-14T04:57:45.3408576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3410359Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3410436Z         
2025-03-14T04:57:45.3410725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3410863Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:57:45.3410936Z         
2025-03-14T04:57:45.3411183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3411694Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3411760Z         
2025-03-14T04:57:45.3412050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3413839Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3413921Z         
2025-03-14T04:57:45.3414207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3414354Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:57:45.3414428Z         
2025-03-14T04:57:45.3414709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3414858Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:57:45.3414924Z         
2025-03-14T04:57:45.3415177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3415668Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3415744Z         
2025-03-14T04:57:45.3416008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3417792Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3417868Z         
2025-03-14T04:57:45.3418164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3418319Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:57:45.3418385Z         
2025-03-14T04:57:45.3418657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3419154Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3419228Z         
2025-03-14T04:57:45.3419521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3421300Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3421380Z         
2025-03-14T04:57:45.3421669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3421817Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:57:45.3421890Z         
2025-03-14T04:57:45.3422161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3422662Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3422730Z         
2025-03-14T04:57:45.3423002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3424911Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3424998Z         
2025-03-14T04:57:45.3425306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3425477Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:57:45.3425549Z         
2025-03-14T04:57:45.3425835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3425995Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:57:45.3426064Z         
2025-03-14T04:57:45.3426365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3426923Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3427004Z         
2025-03-14T04:57:45.3427297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3429235Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3429316Z         
2025-03-14T04:57:45.3429628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3429782Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:57:45.3429852Z         
2025-03-14T04:57:45.3430146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3430672Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3430749Z         
2025-03-14T04:57:45.3431046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3432985Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3433082Z         
2025-03-14T04:57:45.3433381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3433553Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:57:45.3433621Z         
2025-03-14T04:57:45.3433902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3434438Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3434510Z         
2025-03-14T04:57:45.3434792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3436677Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3436755Z         
2025-03-14T04:57:45.3437045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3437206Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:57:45.3437279Z         
2025-03-14T04:57:45.3437563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3437719Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:57:45.3437785Z         
2025-03-14T04:57:45.3438041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3438550Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3438625Z         
2025-03-14T04:57:45.3438886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3440663Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3440765Z         
2025-03-14T04:57:45.3441051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3441198Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:57:45.3441263Z         
2025-03-14T04:57:45.3441520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3442015Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3442090Z         
2025-03-14T04:57:45.3442352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3444159Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3444237Z         
2025-03-14T04:57:45.3444522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3444667Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:57:45.3444733Z         
2025-03-14T04:57:45.3444994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3445503Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3445578Z         
2025-03-14T04:57:45.3445856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3447625Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3447718Z         
2025-03-14T04:57:45.3447999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3448167Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:57:45.3448233Z         
2025-03-14T04:57:45.3448526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3448671Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:57:45.3448745Z         
2025-03-14T04:57:45.3448993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3449503Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3449579Z         
2025-03-14T04:57:45.3449842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3451612Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3451689Z         
2025-03-14T04:57:45.3451986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3452135Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:57:45.3452200Z         
2025-03-14T04:57:45.3452471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3452958Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3453032Z         
2025-03-14T04:57:45.3453308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3455101Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3455177Z         
2025-03-14T04:57:45.3455457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3455611Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:57:45.3455678Z         
2025-03-14T04:57:45.3455978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3456463Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3456537Z         
2025-03-14T04:57:45.3456798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3458593Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3458670Z         
2025-03-14T04:57:45.3458949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3459129Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:57:45.3459195Z         
2025-03-14T04:57:45.3459490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3459634Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:57:45.3459727Z         
2025-03-14T04:57:45.3459978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3460466Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3460541Z         
2025-03-14T04:57:45.3460803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3462601Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3462671Z         
2025-03-14T04:57:45.3462962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3463110Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:57:45.3463178Z         
2025-03-14T04:57:45.3463432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3463930Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3464009Z         
2025-03-14T04:57:45.3464346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3466247Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3466338Z         
2025-03-14T04:57:45.3466628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3466799Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:57:45.3466866Z         
2025-03-14T04:57:45.3467139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3467674Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3467751Z         
2025-03-14T04:57:45.3468035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3469973Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3470053Z         
2025-03-14T04:57:45.3470350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3470521Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:57:45.3470591Z         
2025-03-14T04:57:45.3470893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3471045Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:57:45.3471123Z         
2025-03-14T04:57:45.3471384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3471915Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3471985Z         
2025-03-14T04:57:45.3472270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3474135Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3474229Z         
2025-03-14T04:57:45.3474534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3474670Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:57:45.3474744Z         
2025-03-14T04:57:45.3474991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3475485Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3475559Z         
2025-03-14T04:57:45.3475827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3477623Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3477692Z         
2025-03-14T04:57:45.3477982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3478129Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:57:45.3478193Z         
2025-03-14T04:57:45.3478448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3478952Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3479026Z         
2025-03-14T04:57:45.3479286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3481064Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3481155Z         
2025-03-14T04:57:45.3481404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3482290Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.3482364Z         
2025-03-14T04:57:45.3482638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3484540Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3484617Z         
2025-03-14T04:57:45.3484913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3485058Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:57:45.3485128Z         
2025-03-14T04:57:45.3485411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3485565Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:57:45.3485634Z         
2025-03-14T04:57:45.3485915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3486397Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3486497Z         
2025-03-14T04:57:45.3486759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3488493Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3488595Z         
2025-03-14T04:57:45.3488872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3489014Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:57:45.3489080Z         
2025-03-14T04:57:45.3489334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3489822Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3489896Z         
2025-03-14T04:57:45.3490159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3491891Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3491965Z         
2025-03-14T04:57:45.3492240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3492396Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:57:45.3492462Z         
2025-03-14T04:57:45.3492717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3493231Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3493298Z         
2025-03-14T04:57:45.3493568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3495350Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3495428Z         
2025-03-14T04:57:45.3495717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3495878Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:57:45.3495950Z         
2025-03-14T04:57:45.3496231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3496401Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:57:45.3496467Z         
2025-03-14T04:57:45.3496722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3497193Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.3497266Z         
2025-03-14T04:57:45.3497525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3499294Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3499385Z         
2025-03-14T04:57:45.3499672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3499817Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:57:45.3499884Z         
2025-03-14T04:57:45.3500143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3500636Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.3500711Z         
2025-03-14T04:57:45.3500974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3502733Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3502808Z         
2025-03-14T04:57:45.3503103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3503245Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:57:45.3503309Z         
2025-03-14T04:57:45.3503570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3504128Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.3504215Z         
2025-03-14T04:57:45.3504505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.3506313Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.3506405Z         
2025-03-14T04:57:45.3506685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.3506849Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:57:45.3506939Z         
2025-03-14T04:57:45.3507228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.3507381Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:57:45.3507448Z         
2025-03-14T04:57:45.3507711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3508274Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.3508351Z         
2025-03-14T04:57:45.3508603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3509155Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.3509222Z         
2025-03-14T04:57:45.3509663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.3509933Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:57:45.3510007Z         
2025-03-14T04:57:45.3510262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3510839Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.3510914Z         
2025-03-14T04:57:45.3511262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.3511461Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:57:45.3511525Z         
2025-03-14T04:57:45.3511784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3512358Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.3512453Z         
2025-03-14T04:57:45.3512857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.3513186Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:57:45.3513269Z         
2025-03-14T04:57:45.3513526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3514092Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.3514168Z         
2025-03-14T04:57:45.3514519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.3514727Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:57:45.3514800Z         
2025-03-14T04:57:45.3515050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3515631Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.3515702Z         
2025-03-14T04:57:45.3516127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.3516449Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:57:45.3516523Z         
2025-03-14T04:57:45.3516774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3517351Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.3517418Z         
2025-03-14T04:57:45.3517770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.3517988Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:57:45.3518056Z         
2025-03-14T04:57:45.3518326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3518933Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.3519022Z         
2025-03-14T04:57:45.3519381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:57:45.3519602Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:57:45.3519685Z         
2025-03-14T04:57:45.3520131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3520289Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.3520367Z         
2025-03-14T04:57:45.3520666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3520816Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:45.3520882Z         
2025-03-14T04:57:45.3521321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3521478Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.3521553Z         
2025-03-14T04:57:45.3521847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3522001Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:45.3522065Z         
2025-03-14T04:57:45.3522497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.3522673Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:45.3522780Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:45.3522900Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:45.3522970Z         
2025-03-14T04:57:45.3523289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.3523423Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:45.3523490Z         
2025-03-14T04:57:45.3523818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.3523948Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:45.3524012Z         
2025-03-14T04:57:45.3524399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.3524621Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:45.3524692Z         
2025-03-14T04:57:45.3525097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.3525243Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:45.3525655Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:45.3525784Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:45.3525925Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:57:45.3525995Z         
2025-03-14T04:57:45.3526421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3526580Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.3526646Z         
2025-03-14T04:57:45.3526944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3527088Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:45.3527160Z         
2025-03-14T04:57:45.3527582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3527738Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.3527803Z         
2025-03-14T04:57:45.3528101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3528241Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:57:45.3528315Z         
2025-03-14T04:57:45.3528698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.3528904Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:57:45.3529009Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:57:45.3529145Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:57:45.3529213Z         
2025-03-14T04:57:45.3529547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.3529677Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:57:45.3529752Z         
2025-03-14T04:57:45.3530075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.3530210Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:57:45.3530276Z         
2025-03-14T04:57:45.3530659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.3530914Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:57:45.3530982Z         
2025-03-14T04:57:45.3531400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.3531545Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:57:45.3531973Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:57:45.3532098Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:57:45.3532236Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:57:45.3532302Z         
2025-03-14T04:57:45.3532736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3532886Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.3532958Z         
2025-03-14T04:57:45.3533248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3533394Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:57:45.3533459Z         
2025-03-14T04:57:45.3533889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3534034Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.3534107Z         
2025-03-14T04:57:45.3534395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3534537Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:57:45.3534601Z         
2025-03-14T04:57:45.3534989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.3535182Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:57:45.3535291Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:57:45.3535412Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:57:45.3535486Z         
2025-03-14T04:57:45.3535807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.3535943Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:57:45.3536009Z         
2025-03-14T04:57:45.3536339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.3536467Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:57:45.3536533Z         
2025-03-14T04:57:45.3536915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.3537148Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:57:45.3537222Z         
2025-03-14T04:57:45.3537627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.3537779Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:57:45.3538194Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:57:45.3538336Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:57:45.3538451Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:57:45.3538526Z         
2025-03-14T04:57:45.3538949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3539100Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.3539165Z         
2025-03-14T04:57:45.3539467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3539600Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:57:45.3539673Z         
2025-03-14T04:57:45.3540098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3540249Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.3540315Z         
2025-03-14T04:57:45.3540610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3540759Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:57:45.3540833Z         
2025-03-14T04:57:45.3541202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.3541401Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:57:45.3541502Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:57:45.3541626Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:57:45.3541690Z         
2025-03-14T04:57:45.3542022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.3542146Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:57:45.3542218Z         
2025-03-14T04:57:45.3542542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.3542669Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:57:45.3542737Z         
2025-03-14T04:57:45.3543145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.3543367Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:57:45.3543431Z         
2025-03-14T04:57:45.3543842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.3543985Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:57:45.3544481Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:57:45.3544634Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:57:45.3544767Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:57:45.3544837Z         
2025-03-14T04:57:45.3545286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3545430Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.3545509Z         
2025-03-14T04:57:45.3545800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3545946Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:57:45.3546013Z         
2025-03-14T04:57:45.3546443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.3546585Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.3546664Z         
2025-03-14T04:57:45.3546950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3547115Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:57:45.3547182Z         
2025-03-14T04:57:45.3547558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.3547749Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:57:45.3547862Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:57:45.3547979Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:57:45.3548054Z         
2025-03-14T04:57:45.3548375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.3548506Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:57:45.3548586Z         
2025-03-14T04:57:45.3548927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.3549052Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:57:45.3549118Z         
2025-03-14T04:57:45.3549509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.3549711Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:57:45.3549797Z         
2025-03-14T04:57:45.3550191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.3550318Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:57:45.3550718Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:57:45.3550858Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:57:45.3550968Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:57:45.3551040Z         
2025-03-14T04:57:45.3551335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.3551473Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:57:45.3551607Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:57:45.3551740Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:57:45.3551860Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:57:45.3551987Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:57:45.3552053Z         
2025-03-14T04:57:45.3552323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3552821Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:57:45.3552897Z         
2025-03-14T04:57:45.3553181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.3553386Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:57:45.3553450Z         
2025-03-14T04:57:45.3553828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.3554337Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.3554403Z         
2025-03-14T04:57:45.3554760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.3555272Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:57:45.3555346Z         
2025-03-14T04:57:45.3555610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3556093Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:57:45.3556171Z         
2025-03-14T04:57:45.3556456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.3556660Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:57:45.3556776Z         
2025-03-14T04:57:45.3557144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.3557667Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.3557741Z         
2025-03-14T04:57:45.3558097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.3558616Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:57:45.3558684Z         
2025-03-14T04:57:45.3558945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3559414Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:57:45.3559513Z         
2025-03-14T04:57:45.3559788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.3559976Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:57:45.3560045Z         
2025-03-14T04:57:45.3560422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.3560914Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.3560987Z         
2025-03-14T04:57:45.3561341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.3561855Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:57:45.3561931Z         
2025-03-14T04:57:45.3562181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3562651Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:57:45.3562730Z         
2025-03-14T04:57:45.3563008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.3563189Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:57:45.3563278Z         
2025-03-14T04:57:45.3563653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.3564158Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.3564224Z         
2025-03-14T04:57:45.3564590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.3565089Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:57:45.3565162Z         
2025-03-14T04:57:45.3565421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.3566192Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:45.3566268Z         
2025-03-14T04:57:45.3566539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.3566721Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:57:45.3566787Z         
2025-03-14T04:57:45.3567160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.3568001Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:45.3568077Z         
2025-03-14T04:57:45.3568440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.3569263Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:45.3569357Z         
2025-03-14T04:57:45.3569695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:45.3569884Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:45.3570031Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:45.3570204Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:57:45.3570348Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:57:45.3570509Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:57:45.3570649Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:57:45.3570804Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:57:45.3570940Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:57:45.3571096Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:57:45.3571228Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:57:45.3571301Z         
2025-03-14T04:57:45.3571729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:45.3571910Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:57:45.3572105Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:57:45.3572287Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:57:45.3572455Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:57:45.3572629Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:57:45.3572803Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:57:45.3572951Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:57:45.3573125Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:57:45.3573290Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:57:45.3573438Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:57:45.3573595Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:57:45.3573782Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:57:45.3573923Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:57:45.3574090Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:57:45.3574308Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:57:45.3574384Z         
2025-03-14T04:57:45.3574793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.3575005Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:45.3575087Z         
2025-03-14T04:57:45.3575532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.3575688Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:45.3575850Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:45.3575991Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:45.3576062Z         
2025-03-14T04:57:45.3576436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.3576615Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:45.3576679Z         
2025-03-14T04:57:45.3577008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.3577156Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:45.3577222Z         
2025-03-14T04:57:45.3577538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.3577683Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:45.3577813Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3577961Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:57:45.3578034Z         
2025-03-14T04:57:45.3578341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.3578472Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:45.3578591Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:45.3578747Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:57:45.3578811Z         
2025-03-14T04:57:45.3579119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.3579238Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3579332Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:57:45.3579458Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:57:45.3579529Z         
2025-03-14T04:57:45.3579845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.3579999Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:45.3580105Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:57:45.3580240Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:57:45.3580306Z         
2025-03-14T04:57:45.3580633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.3580787Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.3580927Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:57:45.3580991Z         
2025-03-14T04:57:45.3581295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.3581624Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.3581761Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:57:45.3581825Z         
2025-03-14T04:57:45.3582129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.3582278Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.3582398Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:57:45.3582466Z         
2025-03-14T04:57:45.3582786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.3582974Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:45.3583094Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:57:45.3583161Z         
2025-03-14T04:57:45.3583545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.3583700Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:45.3583768Z         
2025-03-14T04:57:45.3584147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.3584299Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:45.3584373Z         
2025-03-14T04:57:45.3584718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.3584868Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:45.3584996Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:57:45.3585158Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:45.3585300Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:57:45.3585370Z         
2025-03-14T04:57:45.3585713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.3585891Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:45.3586016Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:57:45.3586174Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:45.3586338Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:57:45.3586410Z         
2025-03-14T04:57:45.3586738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.3586867Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:45.3587028Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:45.3587190Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:57:45.3587255Z         
2025-03-14T04:57:45.3587589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.3587708Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:45.3587880Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:45.3588017Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:57:45.3588088Z         
2025-03-14T04:57:45.3588399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.3588508Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:45.3588629Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:45.3588706Z         
2025-03-14T04:57:45.3589012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.3589117Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:45.3589232Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:45.3589308Z         
2025-03-14T04:57:45.3589624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.3589750Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:45.3589878Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:45.3589953Z         
2025-03-14T04:57:45.3590252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.3590375Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:45.3590503Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:45.3590577Z         
2025-03-14T04:57:45.3590919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.3591111Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:45.3591174Z         
2025-03-14T04:57:45.3591517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.3591695Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:57:45.3591768Z         
2025-03-14T04:57:45.3592149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.3592347Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:45.3592420Z         
2025-03-14T04:57:45.3592817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.3593032Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:57:45.3593123Z         
2025-03-14T04:57:45.3593562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.3593716Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:57:45.3593874Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:57:45.3594015Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:57:45.3594088Z         
2025-03-14T04:57:45.3594456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.3594635Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:57:45.3594703Z         
2025-03-14T04:57:45.3595021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.3595169Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:57:45.3595246Z         
2025-03-14T04:57:45.3595554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.3595709Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:57:45.3595838Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3595999Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:57:45.3596066Z         
2025-03-14T04:57:45.3596388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.3596511Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:57:45.3596639Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:57:45.3596795Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:57:45.3596871Z         
2025-03-14T04:57:45.3597182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.3597316Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3597411Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:57:45.3597555Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:57:45.3597620Z         
2025-03-14T04:57:45.3597951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.3598103Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:57:45.3598225Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:57:45.3598356Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:57:45.3598431Z         
2025-03-14T04:57:45.3598732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.3598894Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.3599023Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:57:45.3599095Z         
2025-03-14T04:57:45.3599397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.3599547Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.3599668Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:57:45.3599733Z         
2025-03-14T04:57:45.3600039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.3600187Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.3600304Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:57:45.3600370Z         
2025-03-14T04:57:45.3600677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.3600863Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:57:45.3600981Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:57:45.3601047Z         
2025-03-14T04:57:45.3601402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.3604175Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:57:45.3604251Z         
2025-03-14T04:57:45.3604585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.3604732Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:57:45.3604799Z         
2025-03-14T04:57:45.3605151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.3605294Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.3605432Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:57:45.3605590Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:57:45.3605771Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:57:45.3605846Z         
2025-03-14T04:57:45.3606221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.3606370Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.3606494Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:57:45.3606673Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:57:45.3606813Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:57:45.3606887Z         
2025-03-14T04:57:45.3607215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.3607341Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:57:45.3607503Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:57:45.3607648Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:57:45.3607713Z         
2025-03-14T04:57:45.3608046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.3608169Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:57:45.3608334Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:57:45.3608477Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:57:45.3608542Z         
2025-03-14T04:57:45.3608858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.3608958Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:57:45.3609085Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:57:45.3609150Z         
2025-03-14T04:57:45.3609465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.3609563Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:57:45.3609705Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:57:45.3609772Z         
2025-03-14T04:57:45.3610155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.3610276Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:57:45.3610423Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:57:45.3610490Z         
2025-03-14T04:57:45.3610798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.3610914Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:57:45.3611056Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:57:45.3611122Z         
2025-03-14T04:57:45.3611483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.3611675Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:57:45.3611748Z         
2025-03-14T04:57:45.3612080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.3612248Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:57:45.3612314Z         
2025-03-14T04:57:45.3612694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.3612881Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:57:45.3612955Z         
2025-03-14T04:57:45.3613340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.3613549Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:57:45.3613613Z         
2025-03-14T04:57:45.3614033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.3614183Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:57:45.3614340Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:57:45.3614475Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:57:45.3614548Z         
2025-03-14T04:57:45.3614904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.3615076Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:57:45.3615139Z         
2025-03-14T04:57:45.3615448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.3615598Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:57:45.3615661Z         
2025-03-14T04:57:45.3615986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.3616142Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:57:45.3616272Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3616420Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:57:45.3616489Z         
2025-03-14T04:57:45.3616816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.3616945Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:57:45.3617064Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:57:45.3617219Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:57:45.3617283Z         
2025-03-14T04:57:45.3617591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.3617711Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3617810Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:57:45.3617956Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:57:45.3618029Z         
2025-03-14T04:57:45.3618337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.3618506Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:57:45.3618601Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:57:45.3618738Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:57:45.3618805Z         
2025-03-14T04:57:45.3619118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.3619283Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.3619400Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:57:45.3619476Z         
2025-03-14T04:57:45.3619773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.3619937Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.3620048Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:57:45.3620124Z         
2025-03-14T04:57:45.3620418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.3620574Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.3620684Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:57:45.3620756Z         
2025-03-14T04:57:45.3621060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.3621252Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:57:45.3621365Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:57:45.3621442Z         
2025-03-14T04:57:45.3621790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.3621956Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:57:45.3622020Z         
2025-03-14T04:57:45.3622364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.3622501Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:57:45.3622573Z         
2025-03-14T04:57:45.3622915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.3623059Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.3623185Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:57:45.3623347Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:57:45.3623486Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:57:45.3623561Z         
2025-03-14T04:57:45.3623925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.3624075Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.3624333Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:57:45.3624513Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:57:45.3624666Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:57:45.3624735Z         
2025-03-14T04:57:45.3625083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.3625202Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:57:45.3625382Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:57:45.3625522Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:57:45.3625609Z         
2025-03-14T04:57:45.3625944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.3626068Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:57:45.3626233Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:57:45.3626377Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:57:45.3626443Z         
2025-03-14T04:57:45.3626757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.3626858Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:57:45.3626985Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:57:45.3627050Z         
2025-03-14T04:57:45.3627363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.3627461Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:57:45.3627603Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:57:45.3627695Z         
2025-03-14T04:57:45.3628005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.3628124Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:57:45.3628267Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:57:45.3628335Z         
2025-03-14T04:57:45.3628642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.3628762Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:57:45.3628902Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:57:45.3628972Z         
2025-03-14T04:57:45.3629323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.3629517Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:57:45.3629594Z         
2025-03-14T04:57:45.3629947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.3630117Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:57:45.3630195Z         
2025-03-14T04:57:45.3630584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.3630758Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:57:45.3630833Z         
2025-03-14T04:57:45.3631233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.3631453Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:57:45.3631522Z         
2025-03-14T04:57:45.3631964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.3632124Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:57:45.3632277Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:57:45.3632425Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:57:45.3632493Z         
2025-03-14T04:57:45.3632873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.3633045Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:57:45.3633121Z         
2025-03-14T04:57:45.3633432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.3633587Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:57:45.3633656Z         
2025-03-14T04:57:45.3633988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.3634135Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:57:45.3634267Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3634416Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:57:45.3634490Z         
2025-03-14T04:57:45.3634807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.3634938Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:57:45.3635057Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:57:45.3635215Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:57:45.3635283Z         
2025-03-14T04:57:45.3635606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.3635729Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3635848Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:57:45.3635984Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:57:45.3636058Z         
2025-03-14T04:57:45.3636373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.3636547Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:57:45.3636645Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:57:45.3636784Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:57:45.3636853Z         
2025-03-14T04:57:45.3637174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.3637333Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.3637456Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:57:45.3637523Z         
2025-03-14T04:57:45.3637844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.3638000Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.3638125Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:57:45.3638192Z         
2025-03-14T04:57:45.3638499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.3638650Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.3638773Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:57:45.3638841Z         
2025-03-14T04:57:45.3639152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.3639348Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:57:45.3639462Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:57:45.3639560Z         
2025-03-14T04:57:45.3639904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.3640070Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:57:45.3640138Z         
2025-03-14T04:57:45.3640482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.3640620Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:57:45.3640692Z         
2025-03-14T04:57:45.3641041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.3641188Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.3641317Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:57:45.3641482Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:57:45.3641632Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:57:45.3641703Z         
2025-03-14T04:57:45.3642056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.3642197Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.3642333Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:57:45.3642490Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:57:45.3642626Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:57:45.3642700Z         
2025-03-14T04:57:45.3643019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.3643142Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:57:45.3643299Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:57:45.3643438Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:57:45.3643504Z         
2025-03-14T04:57:45.3643836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.3643948Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:57:45.3644119Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:57:45.3644247Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:57:45.3644322Z         
2025-03-14T04:57:45.3644627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.3644728Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:57:45.3644842Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:57:45.3644917Z         
2025-03-14T04:57:45.3645219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.3645333Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:57:45.3645462Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:57:45.3645534Z         
2025-03-14T04:57:45.3645826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.3645948Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:57:45.3646075Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:57:45.3646147Z         
2025-03-14T04:57:45.3646438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.3646558Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:57:45.3646691Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:57:45.3646757Z         
2025-03-14T04:57:45.3647107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.3647294Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:57:45.3647387Z         
2025-03-14T04:57:45.3647713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.3647880Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:57:45.3647962Z         
2025-03-14T04:57:45.3648362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.3648534Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:57:45.3648609Z         
2025-03-14T04:57:45.3649015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.3649229Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:57:45.3649295Z         
2025-03-14T04:57:45.3649740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.3649891Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:57:45.3650051Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:57:45.3650186Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:57:45.3650262Z         
2025-03-14T04:57:45.3650639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.3650815Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:57:45.3650881Z         
2025-03-14T04:57:45.3651206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.3651374Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:57:45.3651450Z         
2025-03-14T04:57:45.3651782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.3651921Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:57:45.3652044Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3652200Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:57:45.3652266Z         
2025-03-14T04:57:45.3652592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.3652715Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:57:45.3652842Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:57:45.3652991Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:57:45.3653065Z         
2025-03-14T04:57:45.3653374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.3653501Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.3653648Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:57:45.3653781Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:57:45.3653854Z         
2025-03-14T04:57:45.3654166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.3654335Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:57:45.3654427Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:57:45.3654562Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:57:45.3654628Z         
2025-03-14T04:57:45.3654935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.3655089Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.3655209Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:57:45.3655274Z         
2025-03-14T04:57:45.3655579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.3655732Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.3655860Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:57:45.3655925Z         
2025-03-14T04:57:45.3656224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.3656367Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.3656486Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:57:45.3656550Z         
2025-03-14T04:57:45.3656852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.3657032Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:57:45.3657170Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:57:45.3657238Z         
2025-03-14T04:57:45.3657602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.3657741Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:57:45.3657817Z         
2025-03-14T04:57:45.3658145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.3658286Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:57:45.3658351Z         
2025-03-14T04:57:45.3658698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.3658836Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.3658969Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:57:45.3659121Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:57:45.3659268Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:57:45.3659346Z         
2025-03-14T04:57:45.3659697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.3659838Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.3659976Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:57:45.3660132Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:57:45.3660266Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:57:45.3660338Z         
2025-03-14T04:57:45.3660662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.3660785Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:57:45.3660945Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:57:45.3661086Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:57:45.3661153Z         
2025-03-14T04:57:45.3661488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.3661602Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:57:45.3661776Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:57:45.3661905Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:57:45.3661978Z         
2025-03-14T04:57:45.3662286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.3662391Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:57:45.3662506Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:57:45.3662580Z         
2025-03-14T04:57:45.3662901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.3663002Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:57:45.3663134Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:57:45.3663206Z         
2025-03-14T04:57:45.3663509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.3663634Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:57:45.3663767Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:57:45.3663839Z         
2025-03-14T04:57:45.3664213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.3664349Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:57:45.3664483Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:57:45.3664560Z         
2025-03-14T04:57:45.3664912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.3665132Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:57:45.3665201Z         
2025-03-14T04:57:45.3665555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.3665717Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:57:45.3665811Z         
2025-03-14T04:57:45.3666207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.3666386Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:57:45.3666451Z         
2025-03-14T04:57:45.3666938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:45.3667074Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:45.3667147Z         
2025-03-14T04:57:45.3667440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3667592Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:57:45.3667659Z         
2025-03-14T04:57:45.3668096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.3668220Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:57:45.3668325Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:57:45.3668451Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:45.3668516Z         
2025-03-14T04:57:45.3668978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.3669114Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.3669364Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:57:45.3669445Z         
2025-03-14T04:57:45.3669906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.3670077Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.3670151Z         
2025-03-14T04:57:45.3670446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3670578Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:57:45.3670645Z         
2025-03-14T04:57:45.3671083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.3671203Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:57:45.3671321Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:57:45.3671439Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:57:45.3671528Z         
2025-03-14T04:57:45.3671984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.3672151Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.3672386Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:57:45.3672463Z         
2025-03-14T04:57:45.3672916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.3673089Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.3673156Z         
2025-03-14T04:57:45.3673459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3673585Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:57:45.3673660Z         
2025-03-14T04:57:45.3674087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.3674212Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:57:45.3674326Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:57:45.3674443Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:57:45.3674508Z         
2025-03-14T04:57:45.3674964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.3675104Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.3675340Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:57:45.3675412Z         
2025-03-14T04:57:45.3675874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.3676061Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.3676139Z         
2025-03-14T04:57:45.3676437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3676560Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:57:45.3676632Z         
2025-03-14T04:57:45.3677057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.3677180Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:57:45.3677287Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:57:45.3677408Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:57:45.3677474Z         
2025-03-14T04:57:45.3677945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.3678078Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.3678315Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:57:45.3678396Z         
2025-03-14T04:57:45.3678850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.3679013Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.3679087Z         
2025-03-14T04:57:45.3679376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3679505Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:57:45.3679570Z         
2025-03-14T04:57:45.3680000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.3680116Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:57:45.3680229Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:57:45.3680345Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:57:45.3680419Z         
2025-03-14T04:57:45.3680876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.3681040Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:45.3681276Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:57:45.3681344Z         
2025-03-14T04:57:45.3682046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.3682243Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.3682317Z         
2025-03-14T04:57:45.3682608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.3682737Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:57:45.3682803Z         
2025-03-14T04:57:45.3683086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.3683462Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:57:45.3683537Z         
2025-03-14T04:57:45.3683814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.3684302Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:57:45.3684369Z         
2025-03-14T04:57:45.3684648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.3684877Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:57:45.3684950Z         
2025-03-14T04:57:45.3685353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:45.3685508Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:57:45.3685573Z         
2025-03-14T04:57:45.3685882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.3686042Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:57:45.3686107Z         
2025-03-14T04:57:45.3686498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:45.3686639Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:57:45.3686713Z         
2025-03-14T04:57:45.3687215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:45.3687363Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:57:45.3687489Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:45.3687656Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:45.3687791Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:45.3687868Z         
2025-03-14T04:57:45.3688274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:45.3688403Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:45.3688488Z         
2025-03-14T04:57:45.3688953Z 
2025-03-14T04:57:45.3689058Z class GraphModule(torch.nn.Module):
2025-03-14T04:57:45.3810817Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:57:45.3811800Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T04:57:45.3812214Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3812709Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3813173Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3813617Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3814046Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3814461Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3814986Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3815469Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3815947Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3816389Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3816862Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3817397Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3817964Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3818446Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3818998Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3819444Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3819984Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3820526Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3821012Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3821459Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3821920Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.3822444Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3822973Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3823435Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.3823923Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.3824437Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3824968Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3825488Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3825944Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3826365Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3826766Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3827223Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3827690Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3828130Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3829077Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3829466Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3829931Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3830364Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3830762Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3831192Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3831550Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3832002Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3832427Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3832833Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3833232Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3833591Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3834016Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3834416Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3834803Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3835204Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3835580Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3835999Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3836400Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3836796Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3837173Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3837535Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3837968Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3838373Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3838778Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3839154Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3839515Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3839930Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3840350Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3840753Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3841134Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3841489Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3841948Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3842371Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3842769Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3843146Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3843521Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.3843944Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3844384Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3844785Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.3845202Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.3845559Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3845971Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3846386Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3846773Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3847163Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3847511Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3847938Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3848375Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3848761Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3849159Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3849509Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3849926Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3850338Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3850738Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3851157Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3851511Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3851947Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3852355Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3852754Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3853128Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3853486Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3853902Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3854307Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3854697Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3855096Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3855465Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3855872Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3856280Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3856669Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3857046Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3857415Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3857830Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3858262Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3858645Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3859030Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3859388Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3859794Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3860219Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3860608Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3860999Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3861355Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3861807Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3862235Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3862624Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3863005Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3863355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3863770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3864288Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3864726Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3865186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3865571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3866019Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3866455Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3866872Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3867274Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3867666Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3868119Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3868571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3869008Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3869418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3869814Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.3870263Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3870718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3871184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.3871595Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.3871987Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3872427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3872873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3873288Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3873695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3874071Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3874511Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3874949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3875357Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3875816Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3876209Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3876656Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3877090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3877498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3877909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3878299Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3878735Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3879185Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3879591Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3880016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3880384Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3880828Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3881246Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3881844Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3882258Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3882677Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3883103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3883540Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3883936Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3884315Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3884675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3885093Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3885518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3885906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3886955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3887406Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3887815Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3888228Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3888629Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3889015Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3889368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3889768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3890239Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3890649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3891029Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3891409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3891823Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3892240Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3892650Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3893032Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3893404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3893818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3894238Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3894612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3894993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3895341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3895761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3896147Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3896526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3896917Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3897287Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3897703Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3898105Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3898507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3898881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3899250Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3899662Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3900079Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3900469Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3900842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3901200Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3901624Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3902035Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3902434Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3902819Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3903203Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3903629Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3904048Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3904523Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3904928Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3905302Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3905756Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3906172Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3906576Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3906964Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3907328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3907743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3908159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3908552Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3908942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3909299Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3909733Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3910176Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3910594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3910990Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3911345Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3911782Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3912209Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3912647Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3913050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3913422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3913854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3914273Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3914675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3915075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3915442Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3915897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3916339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3916763Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3917143Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3917515Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3917922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3918353Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3918743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3919114Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3919486Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3919892Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3920322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3920713Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3921090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3921444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3921851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3922259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3922639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3923021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3923393Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3923813Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3924225Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3924603Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3924983Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3925329Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3925751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3926161Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3926561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3926942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3927291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3927706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3928111Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3928503Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3928887Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3929242Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3929659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3930079Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3930485Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3930864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3931220Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3931638Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3932038Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3932444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3932817Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3933193Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3933599Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3934008Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3934403Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3934779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3935137Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3935544Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3935952Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3936355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3936771Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3937135Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3937543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3937954Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3938346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3938747Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3939100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3939531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3939942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3940332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3940719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3941074Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3941493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3941900Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3942297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3942699Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3943053Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3943486Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3943895Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3944361Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3944762Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3945132Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3945582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3945997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3946486Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3946881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3947259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3947685Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3948099Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3948501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3948888Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3949256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3949694Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3950133Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3950542Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3950974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3951352Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3951771Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3952217Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3952612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3953024Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3953392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3953815Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3954232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3954634Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3955027Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3955388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3955815Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3956255Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3956651Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3957063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3957421Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3957847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3958264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3958664Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3959101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3959460Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3959910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3960322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3960719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3961090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3961452Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3961869Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3962273Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3962668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3963070Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3963441Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3963856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3964266Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3964668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3965045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3965412Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3965824Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3966275Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3966661Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3967045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3967408Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3967829Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3968250Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3968643Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3969035Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3969427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3969857Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3970302Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3970695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3971099Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3971468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3971906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3972350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3972743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3973168Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3973529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3973963Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3974388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3974795Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3975199Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3975569Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3975994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3976416Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3976821Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3977198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3977557Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3977974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3978377Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3978785Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3979159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3979557Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3979963Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3980372Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3980761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3981135Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3981670Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3982088Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3982504Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3982945Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3983331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3983718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3984161Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3984579Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3984972Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3985396Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3987789Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3988222Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3988660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3989060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3989445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3989831Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3990244Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3990656Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3991052Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3991428Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3991792Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3992226Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3992648Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3993053Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3993434Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3993790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T04:57:45.3994207Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3994676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3995088Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.3995482Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.3995843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T04:57:45.3996269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3996686Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3997151Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.3997561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.3997919Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T04:57:45.3998346Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.3998766Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.3999190Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.3999589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.3999945Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T04:57:45.4000369Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4000802Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4001233Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.4001640Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.4001995Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T04:57:45.4002417Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4002833Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4003235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.4003617Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.4003984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T04:57:45.4004406Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4004818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4005218Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.4005610Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.4005981Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T04:57:45.4006386Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4006797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4007193Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.4007581Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.4007953Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T04:57:45.4008381Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4008791Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4009177Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.4009564Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.4009926Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T04:57:45.4010334Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4010741Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4011122Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.4011508Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.4011854Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T04:57:45.4012284Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4012690Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4013070Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.4013449Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.4013810Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T04:57:45.4014271Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4014694Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4015112Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T04:57:45.4015506Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T04:57:45.4015857Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T04:57:45.4016267Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4016668Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4017054Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.4017431Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.4017786Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T04:57:45.4018198Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4018615Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4019004Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.4019378Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.4019731Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T04:57:45.4020139Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4020566Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4020972Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.4021362Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.4021719Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T04:57:45.4022125Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4022533Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4022918Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T04:57:45.4023303Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T04:57:45.4023660Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T04:57:45.4024068Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4025105Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4025535Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T04:57:45.4025974Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T04:57:45.4026348Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T04:57:45.4026765Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T04:57:45.4027190Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T04:57:45.4027584Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T04:57:45.4027996Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T04:57:45.4028256Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T04:57:45.4028514Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T04:57:45.4028743Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T04:57:45.4028971Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T04:57:45.4029197Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T04:57:45.4029427Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T04:57:45.4029659Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T04:57:45.4029875Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T04:57:45.4030110Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T04:57:45.4030331Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T04:57:45.4030563Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T04:57:45.4030776Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T04:57:45.4031006Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T04:57:45.4031225Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T04:57:45.4031455Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T04:57:45.4031668Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T04:57:45.4032055Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:57:45.4032414Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:57:45.4032842Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:57:45.4033209Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:57:45.4033573Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:57:45.4033912Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:57:45.4034256Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:57:45.4034662Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:57:45.4035053Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:57:45.4035429Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:57:45.4035780Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:57:45.4035865Z         
2025-03-14T04:57:45.4036170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4036741Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4036822Z         
2025-03-14T04:57:45.4037111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4038899Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4038996Z         
2025-03-14T04:57:45.4039311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T04:57:45.4039469Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T04:57:45.4039537Z         
2025-03-14T04:57:45.4039928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T04:57:45.4040180Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T04:57:45.4040257Z         
2025-03-14T04:57:45.4040528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4041051Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4041136Z         
2025-03-14T04:57:45.4041430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4043377Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4043487Z         
2025-03-14T04:57:45.4043830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4043980Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T04:57:45.4044058Z         
2025-03-14T04:57:45.4044328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4044859Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4045008Z         
2025-03-14T04:57:45.4045283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4047078Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4047194Z         
2025-03-14T04:57:45.4047493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4047635Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T04:57:45.4047711Z         
2025-03-14T04:57:45.4047968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4048501Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4048568Z         
2025-03-14T04:57:45.4048862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4050681Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4050749Z         
2025-03-14T04:57:45.4051008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4051517Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.4051590Z         
2025-03-14T04:57:45.4051863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4053737Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4053834Z         
2025-03-14T04:57:45.4054124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4054283Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T04:57:45.4054346Z         
2025-03-14T04:57:45.4054640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4054797Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T04:57:45.4054868Z         
2025-03-14T04:57:45.4055134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4055650Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4055739Z         
2025-03-14T04:57:45.4056005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4057793Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4057870Z         
2025-03-14T04:57:45.4058164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4058318Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T04:57:45.4058386Z         
2025-03-14T04:57:45.4058650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4059144Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4059217Z         
2025-03-14T04:57:45.4059484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4061300Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4061378Z         
2025-03-14T04:57:45.4061665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4061832Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T04:57:45.4061900Z         
2025-03-14T04:57:45.4062160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4062688Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4062792Z         
2025-03-14T04:57:45.4063073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4065154Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4065246Z         
2025-03-14T04:57:45.4065552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4065730Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T04:57:45.4065803Z         
2025-03-14T04:57:45.4066115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4066278Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T04:57:45.4066355Z         
2025-03-14T04:57:45.4066623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4067170Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4067239Z         
2025-03-14T04:57:45.4067526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4069422Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4069516Z         
2025-03-14T04:57:45.4069848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4070000Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T04:57:45.4070080Z         
2025-03-14T04:57:45.4070347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4070874Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4070951Z         
2025-03-14T04:57:45.4071228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4073132Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4073212Z         
2025-03-14T04:57:45.4073515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4073670Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T04:57:45.4073758Z         
2025-03-14T04:57:45.4074032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4074562Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4074635Z         
2025-03-14T04:57:45.4074905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4076749Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4076844Z         
2025-03-14T04:57:45.4077134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4077302Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T04:57:45.4077367Z         
2025-03-14T04:57:45.4077666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4077821Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T04:57:45.4077896Z         
2025-03-14T04:57:45.4078155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4078654Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4078725Z         
2025-03-14T04:57:45.4079009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4080788Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4080883Z         
2025-03-14T04:57:45.4081185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4081334Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T04:57:45.4081409Z         
2025-03-14T04:57:45.4081855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4082371Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4082441Z         
2025-03-14T04:57:45.4082718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4084598Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4084697Z         
2025-03-14T04:57:45.4085000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4085150Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T04:57:45.4085227Z         
2025-03-14T04:57:45.4085483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4085996Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4086073Z         
2025-03-14T04:57:45.4086346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4088158Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4088274Z         
2025-03-14T04:57:45.4088534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4089052Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.4089123Z         
2025-03-14T04:57:45.4089403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4091316Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4091716Z         
2025-03-14T04:57:45.4092011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4092166Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T04:57:45.4092242Z         
2025-03-14T04:57:45.4092527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4092696Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T04:57:45.4092763Z         
2025-03-14T04:57:45.4093028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4093520Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4093592Z         
2025-03-14T04:57:45.4093856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4095659Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4095754Z         
2025-03-14T04:57:45.4096049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4096201Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T04:57:45.4096270Z         
2025-03-14T04:57:45.4096533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4097045Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4097121Z         
2025-03-14T04:57:45.4097412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4099205Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4099289Z         
2025-03-14T04:57:45.4099577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4099731Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T04:57:45.4099795Z         
2025-03-14T04:57:45.4100050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4100559Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4100627Z         
2025-03-14T04:57:45.4100896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4102680Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4102797Z         
2025-03-14T04:57:45.4103087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4103249Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T04:57:45.4103324Z         
2025-03-14T04:57:45.4103644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4103813Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T04:57:45.4103882Z         
2025-03-14T04:57:45.4104243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4104785Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4104864Z         
2025-03-14T04:57:45.4105147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4107049Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4107138Z         
2025-03-14T04:57:45.4107449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4107608Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T04:57:45.4107681Z         
2025-03-14T04:57:45.4107956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4108475Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4108580Z         
2025-03-14T04:57:45.4108862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4110768Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4110850Z         
2025-03-14T04:57:45.4111166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4111345Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T04:57:45.4111413Z         
2025-03-14T04:57:45.4111682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4112205Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4112284Z         
2025-03-14T04:57:45.4112564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4114447Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4114528Z         
2025-03-14T04:57:45.4114829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4115003Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T04:57:45.4115072Z         
2025-03-14T04:57:45.4115382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4115565Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T04:57:45.4115642Z         
2025-03-14T04:57:45.4115909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4116433Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4116510Z         
2025-03-14T04:57:45.4116789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4118686Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4118779Z         
2025-03-14T04:57:45.4119067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4119215Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T04:57:45.4119282Z         
2025-03-14T04:57:45.4119541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4120030Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4120102Z         
2025-03-14T04:57:45.4120364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4122152Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4122252Z         
2025-03-14T04:57:45.4122538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4122687Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T04:57:45.4122752Z         
2025-03-14T04:57:45.4123015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4123508Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4123584Z         
2025-03-14T04:57:45.4123850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4125670Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4125763Z         
2025-03-14T04:57:45.4126055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4126216Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T04:57:45.4126285Z         
2025-03-14T04:57:45.4126583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4126735Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T04:57:45.4126811Z         
2025-03-14T04:57:45.4127059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4127559Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4127636Z         
2025-03-14T04:57:45.4127902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4129686Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4129769Z         
2025-03-14T04:57:45.4130064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4130201Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T04:57:45.4130272Z         
2025-03-14T04:57:45.4130524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4131029Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4131104Z         
2025-03-14T04:57:45.4131381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4133181Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4133259Z         
2025-03-14T04:57:45.4133546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4133690Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T04:57:45.4133754Z         
2025-03-14T04:57:45.4134008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4134494Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4134567Z         
2025-03-14T04:57:45.4134834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4136603Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4136703Z         
2025-03-14T04:57:45.4136952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4137445Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.4137509Z         
2025-03-14T04:57:45.4137794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4139630Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4139717Z         
2025-03-14T04:57:45.4140006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4140147Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T04:57:45.4140220Z         
2025-03-14T04:57:45.4140499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4140648Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T04:57:45.4140712Z         
2025-03-14T04:57:45.4140971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4141451Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4141524Z         
2025-03-14T04:57:45.4141786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4143620Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4143703Z         
2025-03-14T04:57:45.4143999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4144222Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T04:57:45.4144301Z         
2025-03-14T04:57:45.4144627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4145206Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4145299Z         
2025-03-14T04:57:45.4145611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4147467Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4147549Z         
2025-03-14T04:57:45.4147861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4148003Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T04:57:45.4148077Z         
2025-03-14T04:57:45.4172269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4173053Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4173259Z         
2025-03-14T04:57:45.4173583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4175422Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4175508Z         
2025-03-14T04:57:45.4175841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4176010Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T04:57:45.4176074Z         
2025-03-14T04:57:45.4176403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4176580Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T04:57:45.4176654Z         
2025-03-14T04:57:45.4176913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4177409Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4177477Z         
2025-03-14T04:57:45.4177758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4179583Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4179652Z         
2025-03-14T04:57:45.4179956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4180105Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T04:57:45.4180218Z         
2025-03-14T04:57:45.4180484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4180990Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4181057Z         
2025-03-14T04:57:45.4181342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4183517Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4183617Z         
2025-03-14T04:57:45.4183912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4184052Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T04:57:45.4184183Z         
2025-03-14T04:57:45.4184449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4185007Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4185099Z         
2025-03-14T04:57:45.4185380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4187210Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4187277Z         
2025-03-14T04:57:45.4187564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4187753Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T04:57:45.4187820Z         
2025-03-14T04:57:45.4188116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4188261Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T04:57:45.4188332Z         
2025-03-14T04:57:45.4188589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4189080Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4189146Z         
2025-03-14T04:57:45.4189421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4191240Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4191353Z         
2025-03-14T04:57:45.4191653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4191790Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T04:57:45.4191863Z         
2025-03-14T04:57:45.4192117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4192611Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4192676Z         
2025-03-14T04:57:45.4192948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4194713Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4194795Z         
2025-03-14T04:57:45.4195089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4195227Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T04:57:45.4195300Z         
2025-03-14T04:57:45.4195552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4196047Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4196114Z         
2025-03-14T04:57:45.4196403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4198203Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4198289Z         
2025-03-14T04:57:45.4198580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4198729Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T04:57:45.4198804Z         
2025-03-14T04:57:45.4199091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4199237Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T04:57:45.4199301Z         
2025-03-14T04:57:45.4199549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4200019Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4200090Z         
2025-03-14T04:57:45.4200350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4202046Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4202135Z         
2025-03-14T04:57:45.4202413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4202558Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T04:57:45.4202630Z         
2025-03-14T04:57:45.4202880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4203398Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4203481Z         
2025-03-14T04:57:45.4203749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4205486Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4205560Z         
2025-03-14T04:57:45.4205846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4205976Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T04:57:45.4206047Z         
2025-03-14T04:57:45.4206295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4206778Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4206842Z         
2025-03-14T04:57:45.4207108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4208857Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4208924Z         
2025-03-14T04:57:45.4209199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4209342Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T04:57:45.4209430Z         
2025-03-14T04:57:45.4209706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4209876Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T04:57:45.4209956Z         
2025-03-14T04:57:45.4210213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4210673Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4210744Z         
2025-03-14T04:57:45.4210996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4212703Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4212777Z         
2025-03-14T04:57:45.4213055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4213191Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T04:57:45.4213254Z         
2025-03-14T04:57:45.4213503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4213985Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4214055Z         
2025-03-14T04:57:45.4214315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4216040Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4216114Z         
2025-03-14T04:57:45.4216422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4216575Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T04:57:45.4216640Z         
2025-03-14T04:57:45.4216900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4217388Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4217452Z         
2025-03-14T04:57:45.4217726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4219482Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4219558Z         
2025-03-14T04:57:45.4219848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4219992Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T04:57:45.4220115Z         
2025-03-14T04:57:45.4220404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4220551Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T04:57:45.4220618Z         
2025-03-14T04:57:45.4220879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4221355Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4221428Z         
2025-03-14T04:57:45.4221694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4223503Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4223593Z         
2025-03-14T04:57:45.4223882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4224031Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T04:57:45.4224214Z         
2025-03-14T04:57:45.4224499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4225008Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4225088Z         
2025-03-14T04:57:45.4225363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4227151Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4227255Z         
2025-03-14T04:57:45.4227539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4227680Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T04:57:45.4227745Z         
2025-03-14T04:57:45.4228006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4228484Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4228559Z         
2025-03-14T04:57:45.4228821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4230617Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4230706Z         
2025-03-14T04:57:45.4230985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4231137Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T04:57:45.4231202Z         
2025-03-14T04:57:45.4231492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4231638Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T04:57:45.4231702Z         
2025-03-14T04:57:45.4231960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4232430Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4232502Z         
2025-03-14T04:57:45.4232765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4234536Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4234627Z         
2025-03-14T04:57:45.4234910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4235061Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T04:57:45.4235125Z         
2025-03-14T04:57:45.4235379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4235874Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4235947Z         
2025-03-14T04:57:45.4236237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4238027Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4238100Z         
2025-03-14T04:57:45.4238382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4238523Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T04:57:45.4238586Z         
2025-03-14T04:57:45.4238842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4239326Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4239402Z         
2025-03-14T04:57:45.4239663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4241434Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4241526Z         
2025-03-14T04:57:45.4241810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4241966Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T04:57:45.4242032Z         
2025-03-14T04:57:45.4242344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4242486Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T04:57:45.4242557Z         
2025-03-14T04:57:45.4242821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4243323Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4243400Z         
2025-03-14T04:57:45.4243663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4245446Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4245516Z         
2025-03-14T04:57:45.4245800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4245951Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T04:57:45.4246019Z         
2025-03-14T04:57:45.4246271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4246749Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4246844Z         
2025-03-14T04:57:45.4247108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4248888Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4248964Z         
2025-03-14T04:57:45.4249245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4249406Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T04:57:45.4249487Z         
2025-03-14T04:57:45.4249745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4250223Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4250296Z         
2025-03-14T04:57:45.4250559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4252337Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4252411Z         
2025-03-14T04:57:45.4252690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4252853Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T04:57:45.4252918Z         
2025-03-14T04:57:45.4253208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4253377Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T04:57:45.4253448Z         
2025-03-14T04:57:45.4253694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4254179Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4254244Z         
2025-03-14T04:57:45.4254514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4256312Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4256394Z         
2025-03-14T04:57:45.4256687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4256824Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T04:57:45.4256897Z         
2025-03-14T04:57:45.4257145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4257634Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4257709Z         
2025-03-14T04:57:45.4257971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4259748Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4259837Z         
2025-03-14T04:57:45.4260126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4260269Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T04:57:45.4260335Z         
2025-03-14T04:57:45.4260594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4261075Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4261149Z         
2025-03-14T04:57:45.4261413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4263223Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4263310Z         
2025-03-14T04:57:45.4263591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4263753Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T04:57:45.4263817Z         
2025-03-14T04:57:45.4264165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4264318Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T04:57:45.4264392Z         
2025-03-14T04:57:45.4264640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4265151Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4265221Z         
2025-03-14T04:57:45.4265521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4267358Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4267461Z         
2025-03-14T04:57:45.4267774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4267919Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T04:57:45.4267996Z         
2025-03-14T04:57:45.4268259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4268800Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4268869Z         
2025-03-14T04:57:45.4269175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4271079Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4271151Z         
2025-03-14T04:57:45.4271456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4271596Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T04:57:45.4271671Z         
2025-03-14T04:57:45.4271929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4272450Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4272527Z         
2025-03-14T04:57:45.4272803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4274675Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4274775Z         
2025-03-14T04:57:45.4275072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4275244Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T04:57:45.4275321Z         
2025-03-14T04:57:45.4275611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4275766Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T04:57:45.4275837Z         
2025-03-14T04:57:45.4276097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4276596Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4276662Z         
2025-03-14T04:57:45.4276932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4278712Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4278779Z         
2025-03-14T04:57:45.4279071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4279206Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T04:57:45.4279276Z         
2025-03-14T04:57:45.4279527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4280013Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4280096Z         
2025-03-14T04:57:45.4280368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4282412Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4282486Z         
2025-03-14T04:57:45.4282828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4282999Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T04:57:45.4283100Z         
2025-03-14T04:57:45.4283367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4283887Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4283957Z         
2025-03-14T04:57:45.4284253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4286079Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4286146Z         
2025-03-14T04:57:45.4286435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4286595Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T04:57:45.4286659Z         
2025-03-14T04:57:45.4286950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4287132Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T04:57:45.4287205Z         
2025-03-14T04:57:45.4287454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4287940Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4288008Z         
2025-03-14T04:57:45.4288275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4290083Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4290184Z         
2025-03-14T04:57:45.4290477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4290616Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T04:57:45.4290690Z         
2025-03-14T04:57:45.4290940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4291429Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4291495Z         
2025-03-14T04:57:45.4291766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4293548Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4293634Z         
2025-03-14T04:57:45.4293924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4294058Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T04:57:45.4294130Z         
2025-03-14T04:57:45.4294379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4294872Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4294937Z         
2025-03-14T04:57:45.4295207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4297049Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4297131Z         
2025-03-14T04:57:45.4297420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4297573Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T04:57:45.4297645Z         
2025-03-14T04:57:45.4297927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4298079Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T04:57:45.4298142Z         
2025-03-14T04:57:45.4298400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4298885Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4298949Z         
2025-03-14T04:57:45.4299221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4300988Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4301080Z         
2025-03-14T04:57:45.4301370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4301507Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T04:57:45.4301578Z         
2025-03-14T04:57:45.4301822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4302326Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4302392Z         
2025-03-14T04:57:45.4302676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4304515Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4304627Z         
2025-03-14T04:57:45.4304934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4305077Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T04:57:45.4305153Z         
2025-03-14T04:57:45.4305413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4305919Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4305985Z         
2025-03-14T04:57:45.4306259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4308134Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4308225Z         
2025-03-14T04:57:45.4308525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4308685Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T04:57:45.4308760Z         
2025-03-14T04:57:45.4309055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4309226Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T04:57:45.4309294Z         
2025-03-14T04:57:45.4309583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4310106Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4310184Z         
2025-03-14T04:57:45.4310461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4312353Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4312432Z         
2025-03-14T04:57:45.4312734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4312885Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T04:57:45.4312951Z         
2025-03-14T04:57:45.4313221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4313750Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4313836Z         
2025-03-14T04:57:45.4314124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4315914Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4315987Z         
2025-03-14T04:57:45.4316295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4316457Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T04:57:45.4316547Z         
2025-03-14T04:57:45.4316803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4317298Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4317364Z         
2025-03-14T04:57:45.4317636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4319417Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4319493Z         
2025-03-14T04:57:45.4319780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4319930Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T04:57:45.4320003Z         
2025-03-14T04:57:45.4320290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4320460Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T04:57:45.4320524Z         
2025-03-14T04:57:45.4320780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4321263Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4321337Z         
2025-03-14T04:57:45.4321600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4323400Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4323519Z         
2025-03-14T04:57:45.4323808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4323956Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T04:57:45.4324019Z         
2025-03-14T04:57:45.4324282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4324771Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4324845Z         
2025-03-14T04:57:45.4325107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4326885Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4326978Z         
2025-03-14T04:57:45.4327269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4327412Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T04:57:45.4327474Z         
2025-03-14T04:57:45.4327735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4328223Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4328288Z         
2025-03-14T04:57:45.4328557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4330360Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4330449Z         
2025-03-14T04:57:45.4330737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4330887Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T04:57:45.4330957Z         
2025-03-14T04:57:45.4331242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4331392Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T04:57:45.4331458Z         
2025-03-14T04:57:45.4331717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4332197Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4332272Z         
2025-03-14T04:57:45.4332536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4334360Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4334453Z         
2025-03-14T04:57:45.4334735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4334877Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T04:57:45.4334941Z         
2025-03-14T04:57:45.4335193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4335687Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4335760Z         
2025-03-14T04:57:45.4336020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4337824Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4337914Z         
2025-03-14T04:57:45.4338204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4338348Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T04:57:45.4338416Z         
2025-03-14T04:57:45.4338674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4339157Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4339230Z         
2025-03-14T04:57:45.4339497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4341285Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4341378Z         
2025-03-14T04:57:45.4341657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4341815Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T04:57:45.4341880Z         
2025-03-14T04:57:45.4342171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4342344Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T04:57:45.4342410Z         
2025-03-14T04:57:45.4342663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4343157Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4343244Z         
2025-03-14T04:57:45.4343506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4345443Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4345525Z         
2025-03-14T04:57:45.4345828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4345980Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T04:57:45.4346058Z         
2025-03-14T04:57:45.4346318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4346819Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4346918Z         
2025-03-14T04:57:45.4347198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4349099Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4349180Z         
2025-03-14T04:57:45.4349497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4349662Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T04:57:45.4349731Z         
2025-03-14T04:57:45.4350019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4350529Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4350608Z         
2025-03-14T04:57:45.4350882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4352769Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4352849Z         
2025-03-14T04:57:45.4353143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4353309Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T04:57:45.4353376Z         
2025-03-14T04:57:45.4353682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4353848Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T04:57:45.4353932Z         
2025-03-14T04:57:45.4354173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4354647Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4354717Z         
2025-03-14T04:57:45.4354974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4356759Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4356846Z         
2025-03-14T04:57:45.4357132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4357283Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T04:57:45.4357345Z         
2025-03-14T04:57:45.4357604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4358093Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4358165Z         
2025-03-14T04:57:45.4358433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4360239Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4360334Z         
2025-03-14T04:57:45.4360621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4360770Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T04:57:45.4360835Z         
2025-03-14T04:57:45.4361096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4361590Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4361665Z         
2025-03-14T04:57:45.4361928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4363751Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4363839Z         
2025-03-14T04:57:45.4364120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4364287Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T04:57:45.4364352Z         
2025-03-14T04:57:45.4364645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4364793Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T04:57:45.4364869Z         
2025-03-14T04:57:45.4365131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4365652Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4365721Z         
2025-03-14T04:57:45.4366011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4367826Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4367915Z         
2025-03-14T04:57:45.4368208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4368350Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T04:57:45.4368422Z         
2025-03-14T04:57:45.4368670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4369180Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4369254Z         
2025-03-14T04:57:45.4369530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4371338Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4371425Z         
2025-03-14T04:57:45.4371712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4371860Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T04:57:45.4371926Z         
2025-03-14T04:57:45.4372191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4372710Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4372787Z         
2025-03-14T04:57:45.4373068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4374937Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4375033Z         
2025-03-14T04:57:45.4375323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4375502Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T04:57:45.4375573Z         
2025-03-14T04:57:45.4375876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4376046Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T04:57:45.4376123Z         
2025-03-14T04:57:45.4376387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4376937Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4377022Z         
2025-03-14T04:57:45.4377307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4379176Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4379249Z         
2025-03-14T04:57:45.4379561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4379706Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T04:57:45.4379784Z         
2025-03-14T04:57:45.4380048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4380570Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4380663Z         
2025-03-14T04:57:45.4380949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4383016Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4383093Z         
2025-03-14T04:57:45.4383469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4383655Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T04:57:45.4383739Z         
2025-03-14T04:57:45.4384037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4384616Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4384704Z         
2025-03-14T04:57:45.4384998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4386904Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4386989Z         
2025-03-14T04:57:45.4387265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4387429Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T04:57:45.4387496Z         
2025-03-14T04:57:45.4387803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4387985Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T04:57:45.4388064Z         
2025-03-14T04:57:45.4388329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4388850Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4388917Z         
2025-03-14T04:57:45.4389201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4391118Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4391211Z         
2025-03-14T04:57:45.4391523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4391669Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T04:57:45.4391743Z         
2025-03-14T04:57:45.4392010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4392534Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4392603Z         
2025-03-14T04:57:45.4392890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4394762Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4394854Z         
2025-03-14T04:57:45.4395163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4395309Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T04:57:45.4395386Z         
2025-03-14T04:57:45.4395650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4396183Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4396259Z         
2025-03-14T04:57:45.4396536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4398455Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4398539Z         
2025-03-14T04:57:45.4398842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4399016Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T04:57:45.4399083Z         
2025-03-14T04:57:45.4399386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4399540Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T04:57:45.4399613Z         
2025-03-14T04:57:45.4399876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4400402Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4400466Z         
2025-03-14T04:57:45.4400737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4402522Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4402612Z         
2025-03-14T04:57:45.4402907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4403046Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T04:57:45.4403115Z         
2025-03-14T04:57:45.4403366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4403876Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4403943Z         
2025-03-14T04:57:45.4404210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4406010Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4406093Z         
2025-03-14T04:57:45.4406382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4406517Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T04:57:45.4406591Z         
2025-03-14T04:57:45.4406838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4407340Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4407404Z         
2025-03-14T04:57:45.4407674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4409459Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4409544Z         
2025-03-14T04:57:45.4409834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4409991Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T04:57:45.4410065Z         
2025-03-14T04:57:45.4410350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4410518Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T04:57:45.4410583Z         
2025-03-14T04:57:45.4410856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4411342Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4411424Z         
2025-03-14T04:57:45.4411693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4413549Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4413627Z         
2025-03-14T04:57:45.4413936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4414079Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T04:57:45.4414154Z         
2025-03-14T04:57:45.4414416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4414935Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4415027Z         
2025-03-14T04:57:45.4415314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4417077Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4417153Z         
2025-03-14T04:57:45.4417488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4417646Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T04:57:45.4417720Z         
2025-03-14T04:57:45.4417986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4418489Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4418555Z         
2025-03-14T04:57:45.4418838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4420703Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4420775Z         
2025-03-14T04:57:45.4421045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4421569Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T04:57:45.4421647Z         
2025-03-14T04:57:45.4421941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4423913Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4423993Z         
2025-03-14T04:57:45.4424354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4424555Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T04:57:45.4424626Z         
2025-03-14T04:57:45.4424952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4425122Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T04:57:45.4425200Z         
2025-03-14T04:57:45.4425462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4425986Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4426063Z         
2025-03-14T04:57:45.4426342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4428223Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4428303Z         
2025-03-14T04:57:45.4428603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4428754Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T04:57:45.4428822Z         
2025-03-14T04:57:45.4429114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4429627Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4429703Z         
2025-03-14T04:57:45.4429982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4431886Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4431975Z         
2025-03-14T04:57:45.4432262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4432406Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T04:57:45.4432474Z         
2025-03-14T04:57:45.4432729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4433221Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4433296Z         
2025-03-14T04:57:45.4433559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4435330Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4435407Z         
2025-03-14T04:57:45.4435685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4435868Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T04:57:45.4435931Z         
2025-03-14T04:57:45.4436216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4436359Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T04:57:45.4436433Z         
2025-03-14T04:57:45.4436682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4437169Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T04:57:45.4437235Z         
2025-03-14T04:57:45.4437504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4439301Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4439381Z         
2025-03-14T04:57:45.4439672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4439805Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T04:57:45.4439878Z         
2025-03-14T04:57:45.4440125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4440627Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T04:57:45.4440702Z         
2025-03-14T04:57:45.4440965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4442746Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4442838Z         
2025-03-14T04:57:45.4443122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4443264Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T04:57:45.4443330Z         
2025-03-14T04:57:45.4443584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4444076Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T04:57:45.4444152Z         
2025-03-14T04:57:45.4444437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T04:57:45.4446223Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T04:57:45.4446309Z         
2025-03-14T04:57:45.4446588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T04:57:45.4446748Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T04:57:45.4446811Z         
2025-03-14T04:57:45.4447096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T04:57:45.4447236Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T04:57:45.4447309Z         
2025-03-14T04:57:45.4447556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4448121Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.4448186Z         
2025-03-14T04:57:45.4448439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4448967Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T04:57:45.4449053Z         
2025-03-14T04:57:45.4449463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.4449750Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T04:57:45.4449822Z         
2025-03-14T04:57:45.4450065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4450621Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.4450686Z         
2025-03-14T04:57:45.4451053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.4451275Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T04:57:45.4451360Z         
2025-03-14T04:57:45.4451602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4452150Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T04:57:45.4452214Z         
2025-03-14T04:57:45.4452608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.4452920Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T04:57:45.4452991Z         
2025-03-14T04:57:45.4453238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4453785Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.4453858Z         
2025-03-14T04:57:45.4454194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.4454407Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T04:57:45.4454471Z         
2025-03-14T04:57:45.4454721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4455296Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T04:57:45.4455371Z         
2025-03-14T04:57:45.4455759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T04:57:45.4456082Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T04:57:45.4456146Z         
2025-03-14T04:57:45.4456398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4456978Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.4457041Z         
2025-03-14T04:57:45.4457396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T04:57:45.4457618Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T04:57:45.4457688Z         
2025-03-14T04:57:45.4457929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4458534Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T04:57:45.4458598Z         
2025-03-14T04:57:45.4458953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T04:57:45.4459161Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T04:57:45.4459231Z         
2025-03-14T04:57:45.4459654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4459815Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.4459878Z         
2025-03-14T04:57:45.4460175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4460313Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:57:45.4460386Z         
2025-03-14T04:57:45.4460809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4460962Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:57:45.4461049Z         
2025-03-14T04:57:45.4461337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4461481Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:57:45.4461543Z         
2025-03-14T04:57:45.4461916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.4462095Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:57:45.4462202Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:57:45.4462321Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:57:45.4462392Z         
2025-03-14T04:57:45.4462713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.4462853Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:57:45.4462917Z         
2025-03-14T04:57:45.4463271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.4463394Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:57:45.4463465Z         
2025-03-14T04:57:45.4463862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.4464175Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:57:45.4464249Z         
2025-03-14T04:57:45.4464679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.4464822Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:57:45.4465306Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:57:45.4465449Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:57:45.4465593Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:57:45.4465665Z         
2025-03-14T04:57:45.4466163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4466325Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.4466403Z         
2025-03-14T04:57:45.4466715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4466871Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:57:45.4466941Z         
2025-03-14T04:57:45.4467393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4467556Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:57:45.4467656Z         
2025-03-14T04:57:45.4467989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4468134Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:57:45.4468209Z         
2025-03-14T04:57:45.4468596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.4468812Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:57:45.4468923Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:57:45.4469061Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:57:45.4469129Z         
2025-03-14T04:57:45.4469486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.4469615Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:57:45.4469688Z         
2025-03-14T04:57:45.4470031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.4470165Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:57:45.4470245Z         
2025-03-14T04:57:45.4470647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.4470862Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:57:45.4470935Z         
2025-03-14T04:57:45.4471343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.4471483Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:57:45.4471896Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:57:45.4472034Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:57:45.4472151Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:57:45.4472223Z         
2025-03-14T04:57:45.4472648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4472805Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.4472869Z         
2025-03-14T04:57:45.4473165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4473301Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:57:45.4473374Z         
2025-03-14T04:57:45.4473797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4473964Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:57:45.4474035Z         
2025-03-14T04:57:45.4474328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4474477Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:57:45.4474544Z         
2025-03-14T04:57:45.4474936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.4475139Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:57:45.4475253Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:57:45.4475381Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:57:45.4475456Z         
2025-03-14T04:57:45.4475794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.4475931Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:57:45.4475998Z         
2025-03-14T04:57:45.4476357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.4476500Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:57:45.4476592Z         
2025-03-14T04:57:45.4476992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.4477221Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:57:45.4477289Z         
2025-03-14T04:57:45.4477726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.4477859Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:57:45.4478302Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:57:45.4478432Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:57:45.4478559Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T04:57:45.4478627Z         
2025-03-14T04:57:45.4479090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4479242Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.4479317Z         
2025-03-14T04:57:45.4479625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4479774Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:57:45.4479842Z         
2025-03-14T04:57:45.4480300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4480475Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:57:45.4480543Z         
2025-03-14T04:57:45.4480858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4481007Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:57:45.4481087Z         
2025-03-14T04:57:45.4481700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.4481936Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:57:45.4482059Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:57:45.4482193Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:57:45.4482261Z         
2025-03-14T04:57:45.4482619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.4482793Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:57:45.4482868Z         
2025-03-14T04:57:45.4483226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.4483358Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:57:45.4483447Z         
2025-03-14T04:57:45.4483843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.4484063Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:57:45.4484141Z         
2025-03-14T04:57:45.4484566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.4484710Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:57:45.4485138Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:57:45.4485278Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:57:45.4485401Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T04:57:45.4485478Z         
2025-03-14T04:57:45.4485917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4486077Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.4486144Z         
2025-03-14T04:57:45.4486454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4486594Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:57:45.4486669Z         
2025-03-14T04:57:45.4487104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:57:45.4487281Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:57:45.4487354Z         
2025-03-14T04:57:45.4487651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4487794Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:57:45.4487859Z         
2025-03-14T04:57:45.4488244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:57:45.4488440Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:57:45.4488547Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:57:45.4488670Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:57:45.4488741Z         
2025-03-14T04:57:45.4489071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:57:45.4489223Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:57:45.4489291Z         
2025-03-14T04:57:45.4489640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:57:45.4489791Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:57:45.4489863Z         
2025-03-14T04:57:45.4490249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:57:45.4490470Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:57:45.4490535Z         
2025-03-14T04:57:45.4490954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:57:45.4491080Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:57:45.4491507Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:57:45.4491631Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:57:45.4491758Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T04:57:45.4491825Z         
2025-03-14T04:57:45.4492138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.4492273Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T04:57:45.4492413Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T04:57:45.4492543Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T04:57:45.4492678Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T04:57:45.4492799Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T04:57:45.4492872Z         
2025-03-14T04:57:45.4493134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4493673Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T04:57:45.4493750Z         
2025-03-14T04:57:45.4494035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.4494246Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T04:57:45.4494313Z         
2025-03-14T04:57:45.4494710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.4495234Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.4495307Z         
2025-03-14T04:57:45.4495688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.4496238Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T04:57:45.4496319Z         
2025-03-14T04:57:45.4496591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4497085Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T04:57:45.4497159Z         
2025-03-14T04:57:45.4497441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.4497655Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T04:57:45.4497730Z         
2025-03-14T04:57:45.4498104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.4498621Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.4498686Z         
2025-03-14T04:57:45.4499045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.4499554Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T04:57:45.4499629Z         
2025-03-14T04:57:45.4499913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4500391Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T04:57:45.4500455Z         
2025-03-14T04:57:45.4500733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.4500918Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T04:57:45.4500988Z         
2025-03-14T04:57:45.4501364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.4501887Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.4501962Z         
2025-03-14T04:57:45.4502332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.4502849Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T04:57:45.4502916Z         
2025-03-14T04:57:45.4503173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4503641Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T04:57:45.4503714Z         
2025-03-14T04:57:45.4504010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.4504303Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T04:57:45.4504380Z         
2025-03-14T04:57:45.4504802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.4505355Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:57:45.4505438Z         
2025-03-14T04:57:45.4505839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.4506336Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T04:57:45.4506433Z         
2025-03-14T04:57:45.4506686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:57:45.4507446Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:57:45.4507514Z         
2025-03-14T04:57:45.4507793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:57:45.4507967Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T04:57:45.4508044Z         
2025-03-14T04:57:45.4508410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:57:45.4509290Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:57:45.4509378Z         
2025-03-14T04:57:45.4509736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:57:45.4510549Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:57:45.4510613Z         
2025-03-14T04:57:45.4510965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:57:45.4511132Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:57:45.4511283Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:57:45.4511447Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:57:45.4511601Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:57:45.4511756Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:57:45.4511902Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:57:45.4512057Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:57:45.4512194Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:57:45.4512347Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:57:45.4512478Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:57:45.4512565Z         
2025-03-14T04:57:45.4512987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:57:45.4513176Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T04:57:45.4513361Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:57:45.4513550Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:57:45.4513714Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T04:57:45.4513896Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:57:45.4514068Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:57:45.4514224Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T04:57:45.4514406Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:57:45.4514582Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:57:45.4514750Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T04:57:45.4514933Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:57:45.4515096Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:57:45.4515248Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T04:57:45.4515403Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:57:45.4515576Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:57:45.4515643Z         
2025-03-14T04:57:45.4516052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.4516262Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:57:45.4516326Z         
2025-03-14T04:57:45.4516760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.4516920Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:57:45.4517075Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:57:45.4517213Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:57:45.4517283Z         
2025-03-14T04:57:45.4517661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.4517841Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:57:45.4517904Z         
2025-03-14T04:57:45.4518219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.4518385Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:57:45.4518456Z         
2025-03-14T04:57:45.4518769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.4518910Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:57:45.4519039Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4519202Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:57:45.4519266Z         
2025-03-14T04:57:45.4519591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.4519720Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:57:45.4519851Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:57:45.4520005Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:57:45.4520093Z         
2025-03-14T04:57:45.4520403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.4520552Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4520658Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:57:45.4520794Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:57:45.4520859Z         
2025-03-14T04:57:45.4521176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.4521328Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:57:45.4521428Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:57:45.4521559Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:57:45.4521630Z         
2025-03-14T04:57:45.4521965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.4522133Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.4522248Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:57:45.4522322Z         
2025-03-14T04:57:45.4522619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.4522784Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.4522898Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:57:45.4522972Z         
2025-03-14T04:57:45.4523276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.4523429Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.4523549Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:57:45.4523613Z         
2025-03-14T04:57:45.4523917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.4524126Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:57:45.4524244Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:57:45.4524308Z         
2025-03-14T04:57:45.4524650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.4524792Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:57:45.4524866Z         
2025-03-14T04:57:45.4525199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.4525341Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:57:45.4525406Z         
2025-03-14T04:57:45.4525751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.4525889Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:57:45.4526043Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:57:45.4526200Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:57:45.4526362Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:57:45.4526443Z         
2025-03-14T04:57:45.4526794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.4526933Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:57:45.4527065Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:57:45.4527215Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:57:45.4527360Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:57:45.4527424Z         
2025-03-14T04:57:45.4527765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.4527887Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:57:45.4528052Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:57:45.4528183Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:57:45.4528256Z         
2025-03-14T04:57:45.4528585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.4528707Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:57:45.4528873Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:57:45.4529014Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:57:45.4529078Z         
2025-03-14T04:57:45.4529393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.4529492Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:57:45.4529617Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:57:45.4529696Z         
2025-03-14T04:57:45.4530008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.4530102Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:57:45.4530225Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:57:45.4530288Z         
2025-03-14T04:57:45.4530600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.4530722Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:57:45.4530854Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:57:45.4530917Z         
2025-03-14T04:57:45.4531221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.4531344Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:57:45.4531473Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:57:45.4531546Z         
2025-03-14T04:57:45.4531958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.4532171Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:57:45.4532254Z         
2025-03-14T04:57:45.4532600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.4532767Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:57:45.4532843Z         
2025-03-14T04:57:45.4533233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.4533424Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:57:45.4533492Z         
2025-03-14T04:57:45.4533916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.4534129Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:57:45.4534201Z         
2025-03-14T04:57:45.4534627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.4534791Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:57:45.4534942Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:57:45.4535091Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:57:45.4535155Z         
2025-03-14T04:57:45.4535536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.4535708Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:57:45.4535782Z         
2025-03-14T04:57:45.4536088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.4536262Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:57:45.4536327Z         
2025-03-14T04:57:45.4536646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.4536779Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:57:45.4536915Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4537069Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:57:45.4537153Z         
2025-03-14T04:57:45.4537466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.4537600Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:57:45.4537721Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:57:45.4537881Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:57:45.4537969Z         
2025-03-14T04:57:45.4538288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.4538432Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4538543Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:57:45.4538684Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:57:45.4538748Z         
2025-03-14T04:57:45.4539064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.4539215Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:57:45.4539318Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:57:45.4539451Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:57:45.4539521Z         
2025-03-14T04:57:45.4539819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.4539983Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.4540098Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:57:45.4540170Z         
2025-03-14T04:57:45.4540465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.4540627Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.4540741Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:57:45.4540812Z         
2025-03-14T04:57:45.4541110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.4541271Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.4541384Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:57:45.4541457Z         
2025-03-14T04:57:45.4541756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.4541973Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:57:45.4542087Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:57:45.4542160Z         
2025-03-14T04:57:45.4542506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.4542663Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:57:45.4542730Z         
2025-03-14T04:57:45.4543070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.4543211Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:57:45.4543284Z         
2025-03-14T04:57:45.4543629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.4543776Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.4543924Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:57:45.4544155Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:57:45.4544338Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:57:45.4544422Z         
2025-03-14T04:57:45.4544789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.4544934Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:57:45.4545079Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:57:45.4545235Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:57:45.4545385Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:57:45.4545452Z         
2025-03-14T04:57:45.4545788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.4545907Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:57:45.4546075Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:57:45.4546211Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:57:45.4546287Z         
2025-03-14T04:57:45.4546615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.4546738Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:57:45.4546904Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:57:45.4547047Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:57:45.4547112Z         
2025-03-14T04:57:45.4547429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.4547529Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:57:45.4547675Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:57:45.4547739Z         
2025-03-14T04:57:45.4548052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.4548150Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:57:45.4548275Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:57:45.4548340Z         
2025-03-14T04:57:45.4548651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.4548772Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:57:45.4548916Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:57:45.4548980Z         
2025-03-14T04:57:45.4549286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.4549402Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:57:45.4549543Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:57:45.4549624Z         
2025-03-14T04:57:45.4549998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.4550188Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:57:45.4550273Z         
2025-03-14T04:57:45.4550597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.4550772Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:57:45.4550834Z         
2025-03-14T04:57:45.4551215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.4551386Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:57:45.4551456Z         
2025-03-14T04:57:45.4551844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.4552055Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:57:45.4552125Z         
2025-03-14T04:57:45.4552544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.4552699Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:57:45.4552848Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:57:45.4552991Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:57:45.4553053Z         
2025-03-14T04:57:45.4553421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.4553587Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:57:45.4553673Z         
2025-03-14T04:57:45.4553977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.4554127Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:57:45.4554191Z         
2025-03-14T04:57:45.4554506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.4554632Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:57:45.4554761Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4554907Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:57:45.4554976Z         
2025-03-14T04:57:45.4555284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.4555411Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:57:45.4555528Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:57:45.4555698Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:57:45.4555761Z         
2025-03-14T04:57:45.4556083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.4556217Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4556314Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:57:45.4556441Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:57:45.4556512Z         
2025-03-14T04:57:45.4556812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.4556962Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:57:45.4557053Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:57:45.4557186Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:57:45.4557248Z         
2025-03-14T04:57:45.4557547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.4557699Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.4557817Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:57:45.4557882Z         
2025-03-14T04:57:45.4558187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.4558335Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.4558456Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:57:45.4558519Z         
2025-03-14T04:57:45.4558823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.4558980Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.4559089Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:57:45.4559159Z         
2025-03-14T04:57:45.4559455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.4559670Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:57:45.4559780Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:57:45.4559853Z         
2025-03-14T04:57:45.4560191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.4560338Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:57:45.4560402Z         
2025-03-14T04:57:45.4560734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.4560871Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:57:45.4560943Z         
2025-03-14T04:57:45.4561281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.4561440Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.4561567Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:57:45.4561744Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:57:45.4561899Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:57:45.4561972Z         
2025-03-14T04:57:45.4562313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.4562457Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:57:45.4562579Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:57:45.4562740Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:57:45.4562876Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:57:45.4562949Z         
2025-03-14T04:57:45.4563278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.4563404Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:57:45.4563562Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:57:45.4563706Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:57:45.4563771Z         
2025-03-14T04:57:45.4564104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.4564218Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:57:45.4564388Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:57:45.4564519Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:57:45.4564592Z         
2025-03-14T04:57:45.4564896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.4565022Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:57:45.4565145Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:57:45.4565209Z         
2025-03-14T04:57:45.4565527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.4565623Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:57:45.4565743Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:57:45.4565806Z         
2025-03-14T04:57:45.4566113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.4566231Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:57:45.4566371Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:57:45.4566436Z         
2025-03-14T04:57:45.4566742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.4566856Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:57:45.4567009Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:57:45.4567074Z         
2025-03-14T04:57:45.4567439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.4567643Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:57:45.4567715Z         
2025-03-14T04:57:45.4568046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.4568217Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:57:45.4568281Z         
2025-03-14T04:57:45.4568668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.4568844Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:57:45.4568915Z         
2025-03-14T04:57:45.4569312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.4569523Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:57:45.4569588Z         
2025-03-14T04:57:45.4570021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.4570174Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:57:45.4570329Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:57:45.4570468Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:57:45.4570541Z         
2025-03-14T04:57:45.4570909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.4571102Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:57:45.4571169Z         
2025-03-14T04:57:45.4571487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.4571637Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:57:45.4571717Z         
2025-03-14T04:57:45.4572031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.4572170Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:57:45.4572306Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4572456Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:57:45.4572530Z         
2025-03-14T04:57:45.4572844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.4572980Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:57:45.4573117Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:57:45.4573275Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:57:45.4573340Z         
2025-03-14T04:57:45.4573666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.4573802Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4573903Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:57:45.4574032Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:57:45.4574103Z         
2025-03-14T04:57:45.4574411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.4574564Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:57:45.4574660Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:57:45.4574795Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:57:45.4574861Z         
2025-03-14T04:57:45.4575164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.4575314Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.4575435Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:57:45.4575498Z         
2025-03-14T04:57:45.4575802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.4575953Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.4576073Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:57:45.4576136Z         
2025-03-14T04:57:45.4576440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.4576592Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.4576710Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:57:45.4576791Z         
2025-03-14T04:57:45.4577098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.4577282Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:57:45.4577402Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:57:45.4577467Z         
2025-03-14T04:57:45.4577817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.4577962Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:57:45.4578036Z         
2025-03-14T04:57:45.4578379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.4578525Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:57:45.4578588Z         
2025-03-14T04:57:45.4578958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.4579102Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.4579247Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:57:45.4579440Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:57:45.4579584Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:57:45.4579658Z         
2025-03-14T04:57:45.4580006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.4580152Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:57:45.4580276Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:57:45.4580438Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:57:45.4580579Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:57:45.4580652Z         
2025-03-14T04:57:45.4580986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.4581111Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:57:45.4581276Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:57:45.4581591Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:57:45.4581666Z         
2025-03-14T04:57:45.4582024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.4582142Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:57:45.4582323Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:57:45.4582461Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:57:45.4582536Z         
2025-03-14T04:57:45.4582853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.4583000Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:57:45.4583121Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:57:45.4583195Z         
2025-03-14T04:57:45.4583508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.4583613Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:57:45.4583732Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:57:45.4583808Z         
2025-03-14T04:57:45.4584160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.4584300Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:57:45.4584440Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:57:45.4584513Z         
2025-03-14T04:57:45.4584821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.4584975Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:57:45.4585112Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:57:45.4585189Z         
2025-03-14T04:57:45.4585565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.4585795Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:57:45.4585860Z         
2025-03-14T04:57:45.4586209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.4586373Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:57:45.4586446Z         
2025-03-14T04:57:45.4586834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.4587022Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:57:45.4587097Z         
2025-03-14T04:57:45.4587504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:57:45.4587721Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:57:45.4587787Z         
2025-03-14T04:57:45.4588232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:57:45.4588386Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:57:45.4588549Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:57:45.4588689Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:57:45.4588763Z         
2025-03-14T04:57:45.4589140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:57:45.4589335Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:57:45.4589399Z         
2025-03-14T04:57:45.4589719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:57:45.4589868Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:57:45.4589939Z         
2025-03-14T04:57:45.4590259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:57:45.4590400Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:57:45.4590526Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4590683Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:57:45.4590749Z         
2025-03-14T04:57:45.4591077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:57:45.4591201Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:57:45.4591349Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:57:45.4591504Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:57:45.4591592Z         
2025-03-14T04:57:45.4592852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:57:45.4593019Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:57:45.4593126Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:57:45.4593270Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:57:45.4593340Z         
2025-03-14T04:57:45.4593680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:57:45.4593835Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:57:45.4593940Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:57:45.4594073Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:57:45.4594153Z         
2025-03-14T04:57:45.4594475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:57:45.4594641Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:57:45.4594760Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:57:45.4594834Z         
2025-03-14T04:57:45.4595148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:57:45.4595299Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:57:45.4595420Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:57:45.4595485Z         
2025-03-14T04:57:45.4595800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:57:45.4595952Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:57:45.4596071Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:57:45.4596168Z         
2025-03-14T04:57:45.4596504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:57:45.4596701Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:57:45.4596815Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:57:45.4596879Z         
2025-03-14T04:57:45.4597222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:57:45.4597360Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:57:45.4597431Z         
2025-03-14T04:57:45.4597762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:57:45.4597906Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:57:45.4597971Z         
2025-03-14T04:57:45.4598339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:57:45.4598474Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.4598623Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:57:45.4598793Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:57:45.4598944Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:57:45.4599010Z         
2025-03-14T04:57:45.4599361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:57:45.4599494Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:57:45.4599623Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:57:45.4599774Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:57:45.4599919Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:57:45.4599985Z         
2025-03-14T04:57:45.4600328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:57:45.4600446Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:57:45.4600618Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:57:45.4600755Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:57:45.4600828Z         
2025-03-14T04:57:45.4601161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:57:45.4601285Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:57:45.4601461Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:57:45.4601593Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:57:45.4601667Z         
2025-03-14T04:57:45.4601986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:57:45.4602109Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:57:45.4602227Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:57:45.4602300Z         
2025-03-14T04:57:45.4602617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:57:45.4602720Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:57:45.4602839Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:57:45.4602910Z         
2025-03-14T04:57:45.4603219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:57:45.4603345Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:57:45.4603490Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:57:45.4603559Z         
2025-03-14T04:57:45.4603861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:57:45.4603996Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:57:45.4604127Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:57:45.4604197Z         
2025-03-14T04:57:45.4604564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:57:45.4604776Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:57:45.4604841Z         
2025-03-14T04:57:45.4605182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:57:45.4605341Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:57:45.4605412Z         
2025-03-14T04:57:45.4605796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:57:45.4605978Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:57:45.4606043Z         
2025-03-14T04:57:45.4606532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:57:45.4606671Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:57:45.4606745Z         
2025-03-14T04:57:45.4607039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4607195Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:57:45.4607262Z         
2025-03-14T04:57:45.4607717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.4607836Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:57:45.4607951Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:57:45.4608068Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:57:45.4608162Z         
2025-03-14T04:57:45.4608632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.4608778Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.4609012Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:57:45.4609091Z         
2025-03-14T04:57:45.4609569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.4609733Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.4609806Z         
2025-03-14T04:57:45.4610098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4610227Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:57:45.4610289Z         
2025-03-14T04:57:45.4610758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.4610893Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:57:45.4611010Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:57:45.4611132Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:57:45.4611202Z         
2025-03-14T04:57:45.4611657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.4611796Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.4612032Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:57:45.4612105Z         
2025-03-14T04:57:45.4612554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.4612727Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.4612792Z         
2025-03-14T04:57:45.4613094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4613219Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:57:45.4613291Z         
2025-03-14T04:57:45.4613723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.4613849Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:57:45.4613962Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:57:45.4614089Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:57:45.4614155Z         
2025-03-14T04:57:45.4614613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.4614766Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.4615011Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:57:45.4615075Z         
2025-03-14T04:57:45.4615530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.4615707Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.4615770Z         
2025-03-14T04:57:45.4616070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4616195Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:57:45.4616266Z         
2025-03-14T04:57:45.4616706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.4616829Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:57:45.4616948Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:57:45.4617086Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:57:45.4617152Z         
2025-03-14T04:57:45.4617615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.4617752Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:57:45.4617995Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:57:45.4618060Z         
2025-03-14T04:57:45.4618520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.4618685Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.4618759Z         
2025-03-14T04:57:45.4619054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4619187Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:57:45.4619251Z         
2025-03-14T04:57:45.4619686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:57:45.4619801Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:57:45.4619914Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:57:45.4620031Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:57:45.4620107Z         
2025-03-14T04:57:45.4620559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:57:45.4620754Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:57:45.4620994Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:57:45.4621067Z         
2025-03-14T04:57:45.4621528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:57:45.4621701Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:57:45.4621774Z         
2025-03-14T04:57:45.4622074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:57:45.4622207Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:57:45.4622273Z         
2025-03-14T04:57:45.4622565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.4623363Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:57:45.4623442Z         
2025-03-14T04:57:45.4623745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.4624322Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:57:45.4624399Z         
2025-03-14T04:57:45.4624690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:57:45.4624896Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:57:45.4624973Z         
2025-03-14T04:57:45.4625366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:57:45.4625522Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:57:45.4625589Z         
2025-03-14T04:57:45.4625902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:57:45.4626057Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:57:45.4626135Z         
2025-03-14T04:57:45.4626518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:57:45.4626665Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:57:45.4626733Z         
2025-03-14T04:57:45.4627231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:57:45.4627374Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:57:45.4627503Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:57:45.4627716Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:57:45.4627859Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:57:45.4627925Z         
2025-03-14T04:57:45.4628308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:57:45.4628439Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:57:45.4628507Z         
2025-03-14T04:58:08.4172233Z 
2025-03-14T04:58:08.4176915Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:08.4181218Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:58:08.4184367Z         l_features_p2_ = L_features_p2_
2025-03-14T04:58:08.4184609Z         l_features_p3_ = L_features_p3_
2025-03-14T04:58:08.4184864Z         l_features_p4_ = L_features_p4_
2025-03-14T04:58:08.4185121Z         l_features_p5_ = L_features_p5_
2025-03-14T04:58:08.4185359Z         l_features_p6_ = L_features_p6_
2025-03-14T04:58:08.4185835Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:58:08.4186497Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:58:08.4187139Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:58:08.4187758Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:58:08.4188355Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:58:08.4188901Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:58:08.4189407Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:58:08.4189966Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:58:08.4190574Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:58:08.4191163Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:58:08.4191821Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:58:08.4192260Z         
2025-03-14T04:58:08.4192915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4193622Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:58:08.4193913Z         
2025-03-14T04:58:08.4194336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4194869Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:58:08.4195146Z         
2025-03-14T04:58:08.4195707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4197436Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:58:08.4197746Z         
2025-03-14T04:58:08.4198217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4199106Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:58:08.4199389Z         
2025-03-14T04:58:08.4199904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4200524Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:58:08.4200866Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:58:08.4201150Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:58:08.4201393Z         
2025-03-14T04:58:08.4201816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4202339Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:58:08.4202590Z         
2025-03-14T04:58:08.4203008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4203522Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:58:08.4203768Z         
2025-03-14T04:58:08.4204240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4204894Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:58:08.4205228Z         
2025-03-14T04:58:08.4205742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4206345Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:58:08.4206859Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:58:08.4207356Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:58:08.4207689Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:58:08.4207921Z         
2025-03-14T04:58:08.4208447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4209093Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:58:08.4209378Z         
2025-03-14T04:58:08.4209788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4210307Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:58:08.4210583Z         
2025-03-14T04:58:08.4211131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4211791Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:58:08.4212073Z         
2025-03-14T04:58:08.4212497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4213028Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:58:08.4213305Z         
2025-03-14T04:58:08.4213766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4214413Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:58:08.4214768Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:58:08.4215050Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:58:08.4215299Z         
2025-03-14T04:58:08.4215711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4216244Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:58:08.4216503Z         
2025-03-14T04:58:08.4216945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4217481Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:58:08.4217743Z         
2025-03-14T04:58:08.4218236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4218927Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:58:08.4219278Z         
2025-03-14T04:58:08.4219808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4220440Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:58:08.4220966Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:58:08.4221484Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:58:08.4221825Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T04:58:08.4222082Z         
2025-03-14T04:58:08.4222615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4223261Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:58:08.4223535Z         
2025-03-14T04:58:08.4223925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4224517Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:58:08.4224789Z         
2025-03-14T04:58:08.4225335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4226007Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:58:08.4226291Z         
2025-03-14T04:58:08.4226710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4227230Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:58:08.4227544Z         
2025-03-14T04:58:08.4228023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4228715Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:58:08.4229082Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:58:08.4229371Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:58:08.4229639Z         
2025-03-14T04:58:08.4230066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4230615Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:58:08.4230886Z         
2025-03-14T04:58:08.4231312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4231833Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:58:08.4232082Z         
2025-03-14T04:58:08.4232557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4233212Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:58:08.4233545Z         
2025-03-14T04:58:08.4234059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4235786Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:58:08.4236329Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:58:08.4237108Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:58:08.4237558Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T04:58:08.4237813Z         
2025-03-14T04:58:08.4238362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4239027Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:58:08.4239307Z         
2025-03-14T04:58:08.4239708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4240213Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:58:08.4240477Z         
2025-03-14T04:58:08.4241005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4241644Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:58:08.4241915Z         
2025-03-14T04:58:08.4242331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4242826Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:58:08.4243112Z         
2025-03-14T04:58:08.4243609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4244240Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:58:08.4244604Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:58:08.4244893Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:58:08.4245832Z         
2025-03-14T04:58:08.4246297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4246837Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:58:08.4247087Z         
2025-03-14T04:58:08.4247517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4248036Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:58:08.4248288Z         
2025-03-14T04:58:08.4248766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4249427Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:58:08.4249762Z         
2025-03-14T04:58:08.4250273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4250883Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:58:08.4251392Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:58:08.4251888Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:58:08.4252230Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:58:08.4252469Z         
2025-03-14T04:58:08.4253008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4253661Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:58:08.4253937Z         
2025-03-14T04:58:08.4254334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4254848Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:58:08.4255108Z         
2025-03-14T04:58:08.4255626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4256280Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:58:08.4256549Z         
2025-03-14T04:58:08.4256961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4257449Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:58:08.4257720Z         
2025-03-14T04:58:08.4260312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4260985Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:58:08.4261358Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:58:08.4261649Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:58:08.4261933Z         
2025-03-14T04:58:08.4262365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4262888Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:58:08.4263138Z         
2025-03-14T04:58:08.4263563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4264078Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:58:08.4264422Z         
2025-03-14T04:58:08.4264906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4265572Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:58:08.4265900Z         
2025-03-14T04:58:08.4266402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4266991Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:58:08.4267506Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:58:08.4268001Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:58:08.4268393Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:58:08.4268634Z         
2025-03-14T04:58:08.4269033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:58:08.4269529Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:58:08.4269841Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T04:58:08.4270147Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T04:58:08.4270715Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T04:58:08.4271035Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T04:58:08.4271286Z         
2025-03-14T04:58:08.4271659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4272432Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T04:58:08.4272989Z         
2025-03-14T04:58:08.4273444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4274021Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T04:58:08.4274362Z         
2025-03-14T04:58:08.4274843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4276308Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4276885Z         
2025-03-14T04:58:08.4277365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4278220Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T04:58:08.4278765Z         
2025-03-14T04:58:08.4279127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4279855Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T04:58:08.4280396Z         
2025-03-14T04:58:08.4280764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4281290Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T04:58:08.4281908Z         
2025-03-14T04:58:08.4282399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4283253Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4284958Z         
2025-03-14T04:58:08.4285433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4286255Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T04:58:08.4286782Z         
2025-03-14T04:58:08.4287130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4287834Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T04:58:08.4288349Z         
2025-03-14T04:58:08.4288712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4289281Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T04:58:08.4289583Z         
2025-03-14T04:58:08.4290112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4290981Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4291497Z         
2025-03-14T04:58:08.4291951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4292761Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T04:58:08.4293275Z         
2025-03-14T04:58:08.4293621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4294329Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T04:58:08.4294842Z         
2025-03-14T04:58:08.4295203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4295710Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T04:58:08.4296008Z         
2025-03-14T04:58:08.4296470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4297287Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4297790Z         
2025-03-14T04:58:08.4298238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4299074Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T04:58:08.4299590Z         
2025-03-14T04:58:08.4299929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4300809Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:58:08.4301496Z         
2025-03-14T04:58:08.4301860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4302362Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T04:58:08.4302655Z         
2025-03-14T04:58:08.4303143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4304315Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:58:08.4305139Z         
2025-03-14T04:58:08.4305594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4306626Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:58:08.4307350Z         
2025-03-14T04:58:08.4307793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:58:08.4308371Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:58:08.4308746Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:58:08.4309120Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:58:08.4309495Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:58:08.4309864Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:58:08.4310219Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:58:08.4310575Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:58:08.4310927Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:58:08.4311276Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:58:08.4311651Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:58:08.4311925Z         
2025-03-14T04:58:08.4312465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:58:08.4313130Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T04:58:08.4313565Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:58:08.4313999Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:58:08.4314407Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T04:58:08.4314819Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:58:08.4315234Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:58:08.4315647Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T04:58:08.4316023Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:58:08.4316436Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:58:08.4316830Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T04:58:08.4317200Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:58:08.4317589Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:58:08.4317962Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T04:58:08.4318327Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:58:08.4318712Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:58:08.4319006Z         
2025-03-14T04:58:08.4319518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4320199Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:58:08.4320532Z         
2025-03-14T04:58:08.4321079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4321737Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:58:08.4322113Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:58:08.4322468Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:58:08.4322738Z         
2025-03-14T04:58:08.4323220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4323839Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:58:08.4324166Z         
2025-03-14T04:58:08.4327577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4329056Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:58:08.4329576Z         
2025-03-14T04:58:08.4330397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4333569Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4333908Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4334257Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:08.4334542Z         
2025-03-14T04:58:08.4334982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4335552Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4335866Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4336347Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:58:08.4336629Z         
2025-03-14T04:58:08.4337072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4337608Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4337884Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:58:08.4338160Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:58:08.4338408Z         
2025-03-14T04:58:08.4338816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4339349Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:08.4339657Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:58:08.4339947Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:58:08.4340203Z         
2025-03-14T04:58:08.4340647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4341178Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4341524Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:58:08.4341769Z         
2025-03-14T04:58:08.4342175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4342703Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4343046Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:58:08.4343290Z         
2025-03-14T04:58:08.4343691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4344305Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4344770Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:58:08.4345029Z         
2025-03-14T04:58:08.4345448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4346064Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:08.4346437Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:58:08.4346683Z         
2025-03-14T04:58:08.4347134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4347701Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:08.4347982Z         
2025-03-14T04:58:08.4348422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4348975Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:08.4349250Z         
2025-03-14T04:58:08.4349702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4350690Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:08.4351076Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:58:08.4351454Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:08.4351832Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:58:08.4352139Z         
2025-03-14T04:58:08.4352606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4353437Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:08.4353786Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:58:08.4354149Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:08.4354530Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:58:08.4354812Z         
2025-03-14T04:58:08.4355270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4355813Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:08.4356166Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:08.4356541Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:58:08.4356817Z         
2025-03-14T04:58:08.4357237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4357743Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:08.4358082Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:08.4358448Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:58:08.4358712Z         
2025-03-14T04:58:08.4359113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4359590Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:08.4359893Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:08.4360137Z         
2025-03-14T04:58:08.4360543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4361017Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:08.4361280Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:08.4361520Z         
2025-03-14T04:58:08.4361922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4362412Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:08.4362722Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:08.4363233Z         
2025-03-14T04:58:08.4363641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4364365Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:08.4364667Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:08.4364923Z         
2025-03-14T04:58:08.4365391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4366006Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:08.4366590Z         
2025-03-14T04:58:08.4367024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4367590Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:58:08.4367879Z         
2025-03-14T04:58:08.4368357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4368976Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:58:08.4369283Z         
2025-03-14T04:58:08.4369772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4370438Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:58:08.4370766Z         
2025-03-14T04:58:08.4371289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4371934Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:58:08.4372302Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:58:08.4372653Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:58:08.4372918Z         
2025-03-14T04:58:08.4373388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4373988Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:58:08.4374308Z         
2025-03-14T04:58:08.4374713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4375231Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:58:08.4375499Z         
2025-03-14T04:58:08.4375903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4376411Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4376730Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4377067Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:58:08.4377341Z         
2025-03-14T04:58:08.4377748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4378248Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4378557Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4378913Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:58:08.4379187Z         
2025-03-14T04:58:08.4379602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4380133Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4380424Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:58:08.4380718Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:58:08.4380980Z         
2025-03-14T04:58:08.4381390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4382217Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:58:08.4382540Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:58:08.4382839Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:58:08.4383103Z         
2025-03-14T04:58:08.4383523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4384046Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4384440Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:58:08.4384703Z         
2025-03-14T04:58:08.4385113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4385647Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4385995Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:58:08.4386258Z         
2025-03-14T04:58:08.4386663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4387184Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4387516Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:58:08.4387760Z         
2025-03-14T04:58:08.4388161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4388841Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:58:08.4389202Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:58:08.4389446Z         
2025-03-14T04:58:08.4389881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4390432Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:58:08.4390707Z         
2025-03-14T04:58:08.4391143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4391686Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:58:08.4391956Z         
2025-03-14T04:58:08.4392411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4393015Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:58:08.4393357Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:58:08.4393736Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:58:08.4394147Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:58:08.4394428Z         
2025-03-14T04:58:08.4394887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4395463Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:58:08.4395802Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:58:08.4396160Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:58:08.4396537Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:58:08.4396818Z         
2025-03-14T04:58:08.4397268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4397806Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:58:08.4398167Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:58:08.4398552Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:58:08.4398827Z         
2025-03-14T04:58:08.4399273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4399806Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:58:08.4400174Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:58:08.4400560Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:58:08.4400834Z         
2025-03-14T04:58:08.4401261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4401757Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:58:08.4402085Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:58:08.4402341Z         
2025-03-14T04:58:08.4402751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4403247Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:58:08.4403524Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:58:08.4403776Z         
2025-03-14T04:58:08.4404200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4404715Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:58:08.4405048Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:58:08.4405318Z         
2025-03-14T04:58:08.4405734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4406237Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:58:08.4406586Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:58:08.4406856Z         
2025-03-14T04:58:08.4407334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4407983Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:58:08.4408307Z         
2025-03-14T04:58:08.4408753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4409337Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:58:08.4409642Z         
2025-03-14T04:58:08.4410141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4410822Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:58:08.4411156Z         
2025-03-14T04:58:08.4411699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4412429Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:58:08.4412792Z         
2025-03-14T04:58:08.4413352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4414025Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:58:08.4414409Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:58:08.4414779Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:58:08.4415055Z         
2025-03-14T04:58:08.4415542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4416166Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:58:08.4416495Z         
2025-03-14T04:58:08.4416921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4417468Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:58:08.4417754Z         
2025-03-14T04:58:08.4418212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4418745Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4419083Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4419445Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:58:08.4419737Z         
2025-03-14T04:58:08.4420181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4420716Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4421067Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4421420Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:58:08.4421704Z         
2025-03-14T04:58:08.4422139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4422676Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4422967Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:58:08.4423264Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:58:08.4423532Z         
2025-03-14T04:58:08.4423951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4424601Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:58:08.4424927Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:58:08.4425216Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:58:08.4425485Z         
2025-03-14T04:58:08.4425905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4426448Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4426799Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:58:08.4427054Z         
2025-03-14T04:58:08.4427467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4428008Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4428353Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:58:08.4428605Z         
2025-03-14T04:58:08.4429016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4429553Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4429875Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:58:08.4430139Z         
2025-03-14T04:58:08.4430533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4431079Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:58:08.4431428Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:58:08.4431663Z         
2025-03-14T04:58:08.4432089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4432623Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:58:08.4432888Z         
2025-03-14T04:58:08.4433303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4433834Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:58:08.4434094Z         
2025-03-14T04:58:08.4434543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4435086Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:58:08.4435426Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:58:08.4435785Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:58:08.4436139Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:58:08.4436404Z         
2025-03-14T04:58:08.4436846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4437396Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:58:08.4437729Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:58:08.4438081Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:58:08.4438436Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:58:08.4438699Z         
2025-03-14T04:58:08.4439123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4439630Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:58:08.4439969Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:58:08.4440327Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:58:08.4440591Z         
2025-03-14T04:58:08.4441017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4441545Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:58:08.4441881Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:58:08.4442244Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:58:08.4442503Z         
2025-03-14T04:58:08.4443450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4443991Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:58:08.4444274Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:58:08.4444527Z         
2025-03-14T04:58:08.4444932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4445410Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:58:08.4445672Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:58:08.4445921Z         
2025-03-14T04:58:08.4446333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4446824Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:58:08.4447146Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:58:08.4447412Z         
2025-03-14T04:58:08.4447819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4448332Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:58:08.4448648Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:58:08.4448911Z         
2025-03-14T04:58:08.4449382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4450031Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:58:08.4450349Z         
2025-03-14T04:58:08.4450782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4451349Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:58:08.4451643Z         
2025-03-14T04:58:08.4452124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4452750Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:58:08.4453057Z         
2025-03-14T04:58:08.4453556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4454238Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:58:08.4454577Z         
2025-03-14T04:58:08.4455110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4455762Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:58:08.4456132Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:58:08.4456491Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:58:08.4456762Z         
2025-03-14T04:58:08.4457234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4457867Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:58:08.4458167Z         
2025-03-14T04:58:08.4458577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4459101Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:58:08.4459376Z         
2025-03-14T04:58:08.4459790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4460304Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4460632Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4460963Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:58:08.4461237Z         
2025-03-14T04:58:08.4461642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4462155Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4462458Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4462788Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:58:08.4463111Z         
2025-03-14T04:58:08.4463528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4464017Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4464392Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:58:08.4464706Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:58:08.4464996Z         
2025-03-14T04:58:08.4465440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4466030Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:58:08.4466363Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:58:08.4466686Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:58:08.4466959Z         
2025-03-14T04:58:08.4467389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4467928Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4468283Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:58:08.4468537Z         
2025-03-14T04:58:08.4468943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4469475Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4469824Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:58:08.4470077Z         
2025-03-14T04:58:08.4470483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4471017Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4471358Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:58:08.4471638Z         
2025-03-14T04:58:08.4472050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4472626Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:58:08.4473002Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:58:08.4473253Z         
2025-03-14T04:58:08.4473704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4474274Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:58:08.4474560Z         
2025-03-14T04:58:08.4474992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4475535Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:58:08.4475805Z         
2025-03-14T04:58:08.4476759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4477331Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:58:08.4477689Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:58:08.4478060Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:58:08.4478427Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:58:08.4478706Z         
2025-03-14T04:58:08.4479156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4479720Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:58:08.4480056Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:58:08.4480748Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:58:08.4481118Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:58:08.4481395Z         
2025-03-14T04:58:08.4481997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4482523Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:58:08.4482876Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:58:08.4483248Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:58:08.4483519Z         
2025-03-14T04:58:08.4484005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4484531Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:58:08.4484881Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:58:08.4485252Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:58:08.4485515Z         
2025-03-14T04:58:08.4485933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4486870Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:58:08.4487155Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:58:08.4487728Z         
2025-03-14T04:58:08.4488139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4488613Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:58:08.4489142Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:58:08.4489710Z         
2025-03-14T04:58:08.4490120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4490613Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:58:08.4490933Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:58:08.4491197Z         
2025-03-14T04:58:08.4491597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4492130Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:58:08.4492447Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:58:08.4492737Z         
2025-03-14T04:58:08.4493222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4493836Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:58:08.4494146Z         
2025-03-14T04:58:08.4494571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4495138Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:58:08.4495434Z         
2025-03-14T04:58:08.4495916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4496539Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:58:08.4496839Z         
2025-03-14T04:58:08.4497328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4498005Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:58:08.4498331Z         
2025-03-14T04:58:08.4498843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4499470Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:58:08.4499828Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:58:08.4500179Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:58:08.4500445Z         
2025-03-14T04:58:08.4500921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4501557Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:58:08.4501854Z         
2025-03-14T04:58:08.4502258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4502779Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:58:08.4503054Z         
2025-03-14T04:58:08.4503464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4503978Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4504361Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4504713Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:58:08.4504996Z         
2025-03-14T04:58:08.4505421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4505970Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4506285Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4506656Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:58:08.4506956Z         
2025-03-14T04:58:08.4507367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4507861Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4508137Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:58:08.4508422Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:58:08.4508681Z         
2025-03-14T04:58:08.4509083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4509618Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:58:08.4509912Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:58:08.4510192Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:58:08.4510450Z         
2025-03-14T04:58:08.4510855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4511377Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4511709Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:58:08.4511948Z         
2025-03-14T04:58:08.4512345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4512863Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4513192Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:58:08.4513432Z         
2025-03-14T04:58:08.4513825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4514332Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4514684Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:58:08.4514923Z         
2025-03-14T04:58:08.4515321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4515870Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:58:08.4516222Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:58:08.4516461Z         
2025-03-14T04:58:08.4516897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4517444Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:58:08.4517710Z         
2025-03-14T04:58:08.4518142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4518676Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:58:08.4518939Z         
2025-03-14T04:58:08.4519401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4519970Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:58:08.4520323Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:58:08.4520666Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:58:08.4521029Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:58:08.4521301Z         
2025-03-14T04:58:08.4521746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4522300Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:58:08.4522627Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:58:08.4522966Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:58:08.4523321Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:58:08.4523596Z         
2025-03-14T04:58:08.4524025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4524549Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:58:08.4524889Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:58:08.4525247Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:58:08.4525514Z         
2025-03-14T04:58:08.4525951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4526466Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:58:08.4526809Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:58:08.4527170Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:58:08.4527429Z         
2025-03-14T04:58:08.4527841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4528337Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:58:08.4528614Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:58:08.4528849Z         
2025-03-14T04:58:08.4529266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4529727Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:58:08.4529994Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:58:08.4530233Z         
2025-03-14T04:58:08.4530628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4531111Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:58:08.4531423Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:58:08.4531693Z         
2025-03-14T04:58:08.4532106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4532583Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:58:08.4532906Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:58:08.4533159Z         
2025-03-14T04:58:08.4533622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4534227Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:58:08.4534546Z         
2025-03-14T04:58:08.4534982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4535548Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:58:08.4535852Z         
2025-03-14T04:58:08.4536328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4536939Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:58:08.4537237Z         
2025-03-14T04:58:08.4537820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:58:08.4538555Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:58:08.4538820Z         
2025-03-14T04:58:08.4539225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4539740Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:58:08.4540017Z         
2025-03-14T04:58:08.4540567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4541192Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:58:08.4541478Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:58:08.4541797Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:58:08.4542035Z         
2025-03-14T04:58:08.4542602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4543267Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4543701Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:58:08.4544057Z         
2025-03-14T04:58:08.4544694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4545418Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4545723Z         
2025-03-14T04:58:08.4546129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4546665Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:58:08.4546921Z         
2025-03-14T04:58:08.4547487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4548144Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:58:08.4548442Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:58:08.4548743Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:58:08.4548997Z         
2025-03-14T04:58:08.4549570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4550252Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4550703Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:58:08.4551073Z         
2025-03-14T04:58:08.4551643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4552352Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4552659Z         
2025-03-14T04:58:08.4553058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4553560Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:58:08.4553824Z         
2025-03-14T04:58:08.4554365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4555055Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:58:08.4555344Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:58:08.4555641Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:58:08.4555896Z         
2025-03-14T04:58:08.4556464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4557172Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4557651Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:58:08.4558026Z         
2025-03-14T04:58:08.4558578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4559259Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4559544Z         
2025-03-14T04:58:08.4559936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4560875Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:58:08.4561134Z         
2025-03-14T04:58:08.4561720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4562362Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:58:08.4562646Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:58:08.4562956Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:58:08.4563203Z         
2025-03-14T04:58:08.4563753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4564411Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4564852Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:58:08.4565218Z         
2025-03-14T04:58:08.4565773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4566463Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4566759Z         
2025-03-14T04:58:08.4567150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4567636Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:58:08.4567885Z         
2025-03-14T04:58:08.4568418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4569029Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:58:08.4569311Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:58:08.4569596Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:58:08.4569838Z         
2025-03-14T04:58:08.4570398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4571102Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:58:08.4571568Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:58:08.4571928Z         
2025-03-14T04:58:08.4572485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4573165Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4573456Z         
2025-03-14T04:58:08.4574127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4574625Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:58:08.4574882Z         
2025-03-14T04:58:08.4575257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4576005Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:58:08.4576501Z         
2025-03-14T04:58:08.4577142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4577987Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:58:08.4578597Z         
2025-03-14T04:58:08.4578986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4579548Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:58:08.4579884Z         
2025-03-14T04:58:08.4580413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:58:08.4581004Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:58:08.4581278Z         
2025-03-14T04:58:08.4581839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:58:08.4582365Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:58:08.4582653Z         
2025-03-14T04:58:08.4583146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:58:08.4583740Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:58:08.4584019Z         
2025-03-14T04:58:08.4584665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:58:08.4585379Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:58:08.4585792Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:08.4586146Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:58:08.4586521Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:58:08.4586790Z         
2025-03-14T04:58:08.4588252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:58:08.4589152Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:58:08.4589411Z         
2025-03-14T04:58:08.4589507Z 
2025-03-14T04:58:08.4589614Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:08.4592902Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T04:58:08.4595328Z         l_features_p2_ = L_features_p2_
2025-03-14T04:58:08.4595561Z         l_features_p3_ = L_features_p3_
2025-03-14T04:58:08.4595792Z         l_features_p4_ = L_features_p4_
2025-03-14T04:58:08.4596012Z         l_features_p5_ = L_features_p5_
2025-03-14T04:58:08.4596235Z         l_features_p6_ = L_features_p6_
2025-03-14T04:58:08.4596642Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T04:58:08.4597218Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T04:58:08.4597792Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T04:58:08.4598358Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T04:58:08.4598923Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T04:58:08.4599466Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T04:58:08.4599970Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T04:58:08.4600525Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T04:58:08.4601133Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T04:58:08.4601718Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T04:58:08.4602315Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T04:58:08.4602695Z         
2025-03-14T04:58:08.4603261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4603931Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T04:58:08.4604216Z         
2025-03-14T04:58:08.4604619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4605132Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:58:08.4605400Z         
2025-03-14T04:58:08.4605948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4606615Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T04:58:08.4606919Z         
2025-03-14T04:58:08.4607325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4607854Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T04:58:08.4608152Z         
2025-03-14T04:58:08.4608639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4609289Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T04:58:08.4609623Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T04:58:08.4609898Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T04:58:08.4610141Z         
2025-03-14T04:58:08.4610556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4611064Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T04:58:08.4612065Z         
2025-03-14T04:58:08.4612931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4613492Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T04:58:08.4613740Z         
2025-03-14T04:58:08.4614212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4614861Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T04:58:08.4615194Z         
2025-03-14T04:58:08.4616036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4617567Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T04:58:08.4618115Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T04:58:08.4618722Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T04:58:08.4619035Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T04:58:08.4619283Z         
2025-03-14T04:58:08.4619841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4620532Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T04:58:08.4620820Z         
2025-03-14T04:58:08.4621227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4621747Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T04:58:08.4622029Z         
2025-03-14T04:58:08.4622580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4623247Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T04:58:08.4623531Z         
2025-03-14T04:58:08.4623997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4624628Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T04:58:08.4624937Z         
2025-03-14T04:58:08.4625472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4626174Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T04:58:08.4626571Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T04:58:08.4626890Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T04:58:08.4627165Z         
2025-03-14T04:58:08.4627614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4628202Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T04:58:08.4628478Z         
2025-03-14T04:58:08.4628953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4629531Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T04:58:08.4629790Z         
2025-03-14T04:58:08.4630281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4630958Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T04:58:08.4631303Z         
2025-03-14T04:58:08.4631831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4632455Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T04:58:08.4632980Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T04:58:08.4633528Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T04:58:08.4635760Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T04:58:08.4636057Z         
2025-03-14T04:58:08.4637519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4639859Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T04:58:08.4640852Z         
2025-03-14T04:58:08.4641264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4641784Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T04:58:08.4642062Z         
2025-03-14T04:58:08.4642616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4643282Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T04:58:08.4643561Z         
2025-03-14T04:58:08.4644040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4644584Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T04:58:08.4644886Z         
2025-03-14T04:58:08.4645375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4646027Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T04:58:08.4646402Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T04:58:08.4646702Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T04:58:08.4646958Z         
2025-03-14T04:58:08.4647397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4647937Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T04:58:08.4648196Z         
2025-03-14T04:58:08.4648631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4649158Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T04:58:08.4649433Z         
2025-03-14T04:58:08.4649924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4650599Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T04:58:08.4650943Z         
2025-03-14T04:58:08.4651473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4652095Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T04:58:08.4652615Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T04:58:08.4653158Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T04:58:08.4657679Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T04:58:08.4657933Z         
2025-03-14T04:58:08.4658493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4659171Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T04:58:08.4659454Z         
2025-03-14T04:58:08.4659857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4660368Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T04:58:08.4660639Z         
2025-03-14T04:58:08.4661183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4661838Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T04:58:08.4662109Z         
2025-03-14T04:58:08.4662541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4663058Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T04:58:08.4664823Z         
2025-03-14T04:58:08.4665232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4665447Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T04:58:08.4665558Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T04:58:08.4665695Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T04:58:08.4665765Z         
2025-03-14T04:58:08.4666132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4666262Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T04:58:08.4666336Z         
2025-03-14T04:58:08.4666670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4666803Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T04:58:08.4666870Z         
2025-03-14T04:58:08.4667573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4667809Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T04:58:08.4667887Z         
2025-03-14T04:58:08.4668338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4668484Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T04:58:08.4668816Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T04:58:08.4668987Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T04:58:08.4669108Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T04:58:08.4669187Z         
2025-03-14T04:58:08.4669640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4669811Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:58:08.4669879Z         
2025-03-14T04:58:08.4670185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4670328Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T04:58:08.4670406Z         
2025-03-14T04:58:08.4670858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T04:58:08.4671018Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T04:58:08.4671085Z         
2025-03-14T04:58:08.4671418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4671578Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T04:58:08.4671695Z         
2025-03-14T04:58:08.4672091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T04:58:08.4672305Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T04:58:08.4672421Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T04:58:08.4672548Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T04:58:08.4672623Z         
2025-03-14T04:58:08.4672976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T04:58:08.4673116Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T04:58:08.4673183Z         
2025-03-14T04:58:08.4673538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T04:58:08.4673665Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T04:58:08.4673738Z         
2025-03-14T04:58:08.4674142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T04:58:08.4674376Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T04:58:08.4674442Z         
2025-03-14T04:58:08.4674888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T04:58:08.4675021Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T04:58:08.4675351Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T04:58:08.4675496Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T04:58:08.4675624Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T04:58:08.4675691Z         
2025-03-14T04:58:08.4676015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:58:08.4676144Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T04:58:08.4676283Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T04:58:08.4676412Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T04:58:08.4676544Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T04:58:08.4676665Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T04:58:08.4676742Z         
2025-03-14T04:58:08.4677011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4677482Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T04:58:08.4677554Z         
2025-03-14T04:58:08.4677856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4678070Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T04:58:08.4678163Z         
2025-03-14T04:58:08.4678562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4678999Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4679076Z         
2025-03-14T04:58:08.4679448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4679887Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T04:58:08.4679955Z         
2025-03-14T04:58:08.4680229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4680653Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T04:58:08.4680729Z         
2025-03-14T04:58:08.4681014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4681217Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T04:58:08.4681284Z         
2025-03-14T04:58:08.4681955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4682382Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4682557Z         
2025-03-14T04:58:08.4682928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4683350Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T04:58:08.4683428Z         
2025-03-14T04:58:08.4683705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4684114Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T04:58:08.4684183Z         
2025-03-14T04:58:08.4684469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4684687Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T04:58:08.4684765Z         
2025-03-14T04:58:08.4685219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4685663Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4685733Z         
2025-03-14T04:58:08.4686105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4686503Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T04:58:08.4686578Z         
2025-03-14T04:58:08.4686833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4687242Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T04:58:08.4687313Z         
2025-03-14T04:58:08.4687601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4687792Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T04:58:08.4687859Z         
2025-03-14T04:58:08.4688248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4688646Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T04:58:08.4688722Z         
2025-03-14T04:58:08.4689084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4689505Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T04:58:08.4689572Z         
2025-03-14T04:58:08.4689834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T04:58:08.4690416Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T04:58:08.4690495Z         
2025-03-14T04:58:08.4690768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T04:58:08.4690950Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T04:58:08.4691031Z         
2025-03-14T04:58:08.4691421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T04:58:08.4692090Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T04:58:08.4692174Z         
2025-03-14T04:58:08.4692544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T04:58:08.4693145Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T04:58:08.4693220Z         
2025-03-14T04:58:08.4693565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T04:58:08.4693743Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T04:58:08.4693892Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T04:58:08.4694066Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T04:58:08.4694216Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T04:58:08.4694382Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T04:58:08.4694526Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T04:58:08.4694685Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T04:58:08.4694825Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T04:58:08.4694980Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T04:58:08.4695133Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T04:58:08.4695206Z         
2025-03-14T04:58:08.4695636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T04:58:08.4695823Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T04:58:08.4696022Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T04:58:08.4696207Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T04:58:08.4696380Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T04:58:08.4696564Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T04:58:08.4696747Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T04:58:08.4696915Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T04:58:08.4697096Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T04:58:08.4697296Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T04:58:08.4697477Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T04:58:08.4697641Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T04:58:08.4697820Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T04:58:08.4697965Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T04:58:08.4698137Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T04:58:08.4698303Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T04:58:08.4698379Z         
2025-03-14T04:58:08.4698796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4699015Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T04:58:08.4699082Z         
2025-03-14T04:58:08.4699537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4699704Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T04:58:08.4699858Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:58:08.4700011Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:58:08.4700077Z         
2025-03-14T04:58:08.4700466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4700642Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:58:08.4700732Z         
2025-03-14T04:58:08.4701054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4701211Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:58:08.4701279Z         
2025-03-14T04:58:08.4701611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4701749Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4701889Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4702045Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:08.4702117Z         
2025-03-14T04:58:08.4702447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4702585Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4702712Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4702891Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T04:58:08.4702958Z         
2025-03-14T04:58:08.4703300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4703445Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4703543Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:58:08.4703675Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T04:58:08.4703748Z         
2025-03-14T04:58:08.4704073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4704308Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:08.4704411Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:58:08.4704558Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T04:58:08.4704625Z         
2025-03-14T04:58:08.4704996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4705166Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4705299Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T04:58:08.4705369Z         
2025-03-14T04:58:08.4705705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4705864Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4705995Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T04:58:08.4706063Z         
2025-03-14T04:58:08.4706377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4706536Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4706658Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T04:58:08.4706723Z         
2025-03-14T04:58:08.4707047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4707271Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:08.4707389Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T04:58:08.4707465Z         
2025-03-14T04:58:08.4707828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4707990Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:08.4708059Z         
2025-03-14T04:58:08.4708421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4708570Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:08.4708644Z         
2025-03-14T04:58:08.4709003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4709171Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:08.4709305Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T04:58:08.4709493Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:08.4709657Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T04:58:08.4709732Z         
2025-03-14T04:58:08.4710096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4710252Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:08.4710381Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T04:58:08.4710547Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:08.4710691Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T04:58:08.4710766Z         
2025-03-14T04:58:08.4711119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4711255Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:08.4711423Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:08.4711571Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T04:58:08.4711638Z         
2025-03-14T04:58:08.4711994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4712118Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:08.4712305Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:08.4712450Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T04:58:08.4712529Z         
2025-03-14T04:58:08.4712855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4712967Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:08.4713112Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:08.4713189Z         
2025-03-14T04:58:08.4713513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4713625Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:08.4713746Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:08.4713825Z         
2025-03-14T04:58:08.4714147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4714281Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:08.4714418Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:08.4714498Z         
2025-03-14T04:58:08.4714817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4714942Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:08.4715092Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:08.4715169Z         
2025-03-14T04:58:08.4715554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4715769Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:08.4715844Z         
2025-03-14T04:58:08.4716193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4716376Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:58:08.4716445Z         
2025-03-14T04:58:08.4716855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4717041Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:58:08.4717115Z         
2025-03-14T04:58:08.4717532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4717762Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T04:58:08.4717831Z         
2025-03-14T04:58:08.4718292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4718459Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T04:58:08.4718625Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:58:08.4718781Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:58:08.4718854Z         
2025-03-14T04:58:08.4719232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4719410Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:58:08.4719492Z         
2025-03-14T04:58:08.4719821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4719970Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:58:08.4720047Z         
2025-03-14T04:58:08.4720368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4720516Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4720650Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4720814Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T04:58:08.4720880Z         
2025-03-14T04:58:08.4721212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4721340Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4721493Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4722205Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T04:58:08.4722293Z         
2025-03-14T04:58:08.4722633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4722787Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4722889Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:58:08.4723038Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T04:58:08.4723113Z         
2025-03-14T04:58:08.4723448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4723604Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:58:08.4723714Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:58:08.4723854Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T04:58:08.4723930Z         
2025-03-14T04:58:08.4724253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4724415Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4724543Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T04:58:08.4724611Z         
2025-03-14T04:58:08.4724927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4725082Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4725208Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T04:58:08.4725274Z         
2025-03-14T04:58:08.4725619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4725774Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4725895Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T04:58:08.4725961Z         
2025-03-14T04:58:08.4726294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4726484Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:58:08.4726610Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T04:58:08.4726676Z         
2025-03-14T04:58:08.4727028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4727174Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:58:08.4727248Z         
2025-03-14T04:58:08.4727586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4727741Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:58:08.4727805Z         
2025-03-14T04:58:08.4728166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4728325Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:58:08.4728464Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T04:58:08.4728643Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:58:08.4728813Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T04:58:08.4728880Z         
2025-03-14T04:58:08.4729241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4729385Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:58:08.4729522Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T04:58:08.4729688Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:58:08.4729833Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T04:58:08.4729908Z         
2025-03-14T04:58:08.4730245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4730375Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:58:08.4730542Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:58:08.4730688Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T04:58:08.4730756Z         
2025-03-14T04:58:08.4731099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4731218Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:58:08.4731397Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:58:08.4731536Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T04:58:08.4731609Z         
2025-03-14T04:58:08.4731924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4732062Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:58:08.4732189Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:58:08.4732262Z         
2025-03-14T04:58:08.4732573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4732678Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:58:08.4732796Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:58:08.4732871Z         
2025-03-14T04:58:08.4733184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4733311Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:58:08.4733449Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:58:08.4733524Z         
2025-03-14T04:58:08.4733830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4733968Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:58:08.4734104Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:58:08.4734178Z         
2025-03-14T04:58:08.4734552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4734774Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:58:08.4735228Z         
2025-03-14T04:58:08.4735598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4735771Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:58:08.4735851Z         
2025-03-14T04:58:08.4736259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4736697Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:58:08.4736777Z         
2025-03-14T04:58:08.4737195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4737409Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T04:58:08.4737482Z         
2025-03-14T04:58:08.4737928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4738097Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T04:58:08.4738263Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:58:08.4738411Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:58:08.4738489Z         
2025-03-14T04:58:08.4738878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4739091Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T04:58:08.4739159Z         
2025-03-14T04:58:08.4739494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4739647Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:58:08.4739723Z         
2025-03-14T04:58:08.4740047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4740190Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4740321Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4740486Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T04:58:08.4740554Z         
2025-03-14T04:58:08.4740890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4741019Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4741170Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4741330Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T04:58:08.4741422Z         
2025-03-14T04:58:08.4741763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4741899Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4741996Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:58:08.4742146Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T04:58:08.4742212Z         
2025-03-14T04:58:08.4742550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4742714Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:58:08.4742826Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:58:08.4742970Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T04:58:08.4743050Z         
2025-03-14T04:58:08.4743389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4743569Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4743704Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T04:58:08.4743780Z         
2025-03-14T04:58:08.4744091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4744324Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4744449Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T04:58:08.4744530Z         
2025-03-14T04:58:08.4744856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4745029Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4745161Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T04:58:08.4745260Z         
2025-03-14T04:58:08.4745608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4745804Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:58:08.4745929Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T04:58:08.4745997Z         
2025-03-14T04:58:08.4746376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4746535Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:58:08.4746615Z         
2025-03-14T04:58:08.4746985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4747148Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:58:08.4747218Z         
2025-03-14T04:58:08.4747626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4747776Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:58:08.4747940Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T04:58:08.4748129Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:58:08.4748300Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T04:58:08.4748368Z         
2025-03-14T04:58:08.4748734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4748877Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:58:08.4749011Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T04:58:08.4749167Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:58:08.4749317Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T04:58:08.4749384Z         
2025-03-14T04:58:08.4750076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4750205Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:58:08.4750387Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:58:08.4750532Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T04:58:08.4750609Z         
2025-03-14T04:58:08.4750957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4751085Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:58:08.4751260Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:58:08.4751410Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T04:58:08.4751478Z         
2025-03-14T04:58:08.4751807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4751953Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:58:08.4752087Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:58:08.4752153Z         
2025-03-14T04:58:08.4752488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4752591Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:58:08.4752721Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:58:08.4752790Z         
2025-03-14T04:58:08.4753118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4753242Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:58:08.4753393Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:58:08.4753460Z         
2025-03-14T04:58:08.4753789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4753941Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:58:08.4754080Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:58:08.4754173Z         
2025-03-14T04:58:08.4754537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4754766Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:58:08.4754836Z         
2025-03-14T04:58:08.4755194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4755365Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:58:08.4755442Z         
2025-03-14T04:58:08.4755845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4756043Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:58:08.4756115Z         
2025-03-14T04:58:08.4756540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4756760Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T04:58:08.4756836Z         
2025-03-14T04:58:08.4757289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4757456Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T04:58:08.4757618Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:58:08.4757773Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:58:08.4757841Z         
2025-03-14T04:58:08.4758238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4758434Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T04:58:08.4758510Z         
2025-03-14T04:58:08.4758834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4758995Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:58:08.4759061Z         
2025-03-14T04:58:08.4759409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4759545Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4759680Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4759835Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T04:58:08.4759910Z         
2025-03-14T04:58:08.4760232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4760386Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4760515Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4760696Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T04:58:08.4760777Z         
2025-03-14T04:58:08.4761104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4761235Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4761331Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:58:08.4761476Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T04:58:08.4761540Z         
2025-03-14T04:58:08.4761868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4762020Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:58:08.4762258Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:58:08.4762435Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T04:58:08.4762556Z         
2025-03-14T04:58:08.4762893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4763112Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4763493Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T04:58:08.4763605Z         
2025-03-14T04:58:08.4763965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4764146Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4781385Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T04:58:08.4781906Z         
2025-03-14T04:58:08.4782342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4782527Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4782865Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T04:58:08.4782945Z         
2025-03-14T04:58:08.4783289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4783502Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:58:08.4783626Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T04:58:08.4783700Z         
2025-03-14T04:58:08.4784067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4784300Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:58:08.4784381Z         
2025-03-14T04:58:08.4784787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4784945Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:58:08.4785038Z         
2025-03-14T04:58:08.4785474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4785669Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:58:08.4785860Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T04:58:08.4786031Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:58:08.4786192Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T04:58:08.4786265Z         
2025-03-14T04:58:08.4786648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4786797Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:58:08.4786940Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T04:58:08.4787112Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:58:08.4787272Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T04:58:08.4787345Z         
2025-03-14T04:58:08.4787705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4787836Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:58:08.4788021Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:58:08.4788168Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T04:58:08.4788248Z         
2025-03-14T04:58:08.4788602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4788737Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:58:08.4788916Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:58:08.4789068Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T04:58:08.4789138Z         
2025-03-14T04:58:08.4789495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4789603Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:58:08.4789740Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:58:08.4789811Z         
2025-03-14T04:58:08.4790153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4790253Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:58:08.4790391Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:58:08.4790461Z         
2025-03-14T04:58:08.4790802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4790927Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:58:08.4791072Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:58:08.4791137Z         
2025-03-14T04:58:08.4791486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4791606Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:58:08.4791785Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:58:08.4791872Z         
2025-03-14T04:58:08.4792259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4792466Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:58:08.4792546Z         
2025-03-14T04:58:08.4792904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4793101Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:58:08.4793169Z         
2025-03-14T04:58:08.4793581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4795585Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:58:08.4796008Z         
2025-03-14T04:58:08.4797443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:08.4798732Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T04:58:08.4798819Z         
2025-03-14T04:58:08.4799911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:08.4800084Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T04:58:08.4800633Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:58:08.4800793Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:58:08.4800866Z         
2025-03-14T04:58:08.4801265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:08.4801503Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T04:58:08.4801579Z         
2025-03-14T04:58:08.4801901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:08.4802062Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:58:08.4802131Z         
2025-03-14T04:58:08.4802463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:08.4802599Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:58:08.4802739Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4802893Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T04:58:08.4802970Z         
2025-03-14T04:58:08.4803321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:08.4803457Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:58:08.4803596Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:58:08.4803759Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T04:58:08.4803849Z         
2025-03-14T04:58:08.4804178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:08.4804305Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:08.4804408Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:58:08.4804544Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T04:58:08.4804620Z         
2025-03-14T04:58:08.4804943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:08.4805105Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:58:08.4805202Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:58:08.4805345Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T04:58:08.4805413Z         
2025-03-14T04:58:08.4805739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:08.4805901Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:08.4806028Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T04:58:08.4806096Z         
2025-03-14T04:58:08.4806419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:08.4806580Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:08.4806711Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T04:58:08.4806780Z         
2025-03-14T04:58:08.4807096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:08.4807256Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:08.4807385Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T04:58:08.4807461Z         
2025-03-14T04:58:08.4807765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:08.4807961Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:58:08.4808074Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T04:58:08.4808150Z         
2025-03-14T04:58:08.4808493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:08.4808642Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:58:08.4808711Z         
2025-03-14T04:58:08.4809059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:08.4809195Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:58:08.4809269Z         
2025-03-14T04:58:08.4809636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:08.4809804Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:58:08.4809947Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T04:58:08.4810112Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:58:08.4810254Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T04:58:08.4810330Z         
2025-03-14T04:58:08.4810681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:08.4810829Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:58:08.4810956Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T04:58:08.4811117Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:58:08.4811256Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T04:58:08.4811332Z         
2025-03-14T04:58:08.4811672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:08.4811803Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:58:08.4811969Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:58:08.4812112Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T04:58:08.4812183Z         
2025-03-14T04:58:08.4812536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:08.4812654Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:58:08.4812834Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:58:08.4812969Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T04:58:08.4813063Z         
2025-03-14T04:58:08.4813805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:08.4813929Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:58:08.4814060Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:58:08.4814135Z         
2025-03-14T04:58:08.4814464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:08.4814567Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:58:08.4814699Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:58:08.4814768Z         
2025-03-14T04:58:08.4815085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:08.4815206Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:58:08.4815355Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:58:08.4815422Z         
2025-03-14T04:58:08.4815761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:08.4815879Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:58:08.4816037Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:58:08.4816120Z         
2025-03-14T04:58:08.4816487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:08.4816680Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:58:08.4816757Z         
2025-03-14T04:58:08.4817096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:08.4817271Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:58:08.4817339Z         
2025-03-14T04:58:08.4817739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:08.4817921Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:58:08.4817999Z         
2025-03-14T04:58:08.4818498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:58:08.4818654Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:58:08.4818724Z         
2025-03-14T04:58:08.4819039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4819189Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T04:58:08.4819268Z         
2025-03-14T04:58:08.4819713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4819841Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T04:58:08.4819968Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:58:08.4820095Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:58:08.4820162Z         
2025-03-14T04:58:08.4820648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4820787Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4821037Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T04:58:08.4821105Z         
2025-03-14T04:58:08.4821595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4821771Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4821845Z         
2025-03-14T04:58:08.4822147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4822314Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:58:08.4822384Z         
2025-03-14T04:58:08.4822853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4823003Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T04:58:08.4823115Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:58:08.4823243Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:58:08.4823311Z         
2025-03-14T04:58:08.4823776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4823912Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4824229Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T04:58:08.4824309Z         
2025-03-14T04:58:08.4824797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4824971Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4825052Z         
2025-03-14T04:58:08.4825663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4825814Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:58:08.4825885Z         
2025-03-14T04:58:08.4826347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4826467Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T04:58:08.4826588Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:58:08.4826713Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:58:08.4826817Z         
2025-03-14T04:58:08.4827294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4827441Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4827694Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T04:58:08.4827774Z         
2025-03-14T04:58:08.4828245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4828431Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4828502Z         
2025-03-14T04:58:08.4828817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4828950Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:58:08.4829028Z         
2025-03-14T04:58:08.4829493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4829645Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T04:58:08.4829779Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:58:08.4829903Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:58:08.4829973Z         
2025-03-14T04:58:08.4830454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4830606Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:08.4830855Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T04:58:08.4830930Z         
2025-03-14T04:58:08.4831401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4831582Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4831651Z         
2025-03-14T04:58:08.4831966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4832098Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:58:08.4832174Z         
2025-03-14T04:58:08.4832620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:08.4832745Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T04:58:08.4832855Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:58:08.4832985Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:58:08.4833053Z         
2025-03-14T04:58:08.4833528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:08.4833716Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:58:08.4833972Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T04:58:08.4834040Z         
2025-03-14T04:58:08.4834519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:08.4834690Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:08.4834768Z         
2025-03-14T04:58:08.4835073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:08.4835208Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:58:08.4835277Z         
2025-03-14T04:58:08.4835579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4836030Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:58:08.4836117Z         
2025-03-14T04:58:08.4836419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4836903Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:58:08.4836981Z         
2025-03-14T04:58:08.4837273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:08.4837493Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T04:58:08.4837561Z         
2025-03-14T04:58:08.4837976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:58:08.4838128Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:58:08.4838208Z         
2025-03-14T04:58:08.4838523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:58:08.4838692Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T04:58:08.4838780Z         
2025-03-14T04:58:08.4839188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:58:08.4839336Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:58:08.4839418Z         
2025-03-14T04:58:08.4839929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:58:08.4840085Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T04:58:08.4840239Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:08.4840406Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:58:08.4840540Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:58:08.4840620Z         
2025-03-14T04:58:08.4840995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:58:08.4841127Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:58:08.4841196Z         
2025-03-14T04:58:13.9078605Z 
2025-03-14T04:58:13.9079655Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:13.9082301Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T04:58:13.9084127Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T04:58:13.9084422Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T04:58:13.9084719Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T04:58:13.9085083Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T04:58:13.9085375Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T04:58:13.9085698Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T04:58:13.9085978Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T04:58:13.9086245Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T04:58:13.9086586Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T04:58:13.9086856Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T04:58:13.9087162Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T04:58:13.9087547Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T04:58:13.9087869Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T04:58:13.9088276Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T04:58:13.9088585Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T04:58:13.9088918Z         
2025-03-14T04:58:13.9089564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:13.9090474Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T04:58:13.9090855Z         
2025-03-14T04:58:13.9091563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:13.9092468Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T04:58:13.9092999Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T04:58:13.9093389Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T04:58:13.9093819Z         
2025-03-14T04:58:13.9094434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:13.9095196Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T04:58:13.9095516Z         
2025-03-14T04:58:13.9096064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:13.9096689Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T04:58:13.9097003Z         
2025-03-14T04:58:13.9097535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:13.9098105Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:13.9098536Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9098921Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T04:58:13.9099287Z         
2025-03-14T04:58:13.9099807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:13.9100376Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:13.9100802Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:13.9101154Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T04:58:13.9101528Z         
2025-03-14T04:58:13.9101954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:13.9102565Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9102893Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T04:58:13.9103200Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T04:58:13.9103480Z         
2025-03-14T04:58:13.9103976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:13.9104771Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:13.9105128Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T04:58:13.9105502Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T04:58:13.9105785Z         
2025-03-14T04:58:13.9106310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:13.9106923Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:13.9107270Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T04:58:13.9107565Z         
2025-03-14T04:58:13.9107988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:13.9108620Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:13.9108979Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T04:58:13.9109301Z         
2025-03-14T04:58:13.9109716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:13.9110350Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:13.9110760Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T04:58:13.9111012Z         
2025-03-14T04:58:13.9111504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:13.9112084Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:13.9112516Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T04:58:13.9112765Z         
2025-03-14T04:58:13.9113286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:13.9113920Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:13.9114198Z         
2025-03-14T04:58:13.9114730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:13.9115371Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:13.9115644Z         
2025-03-14T04:58:13.9116211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:13.9116837Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:13.9117188Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T04:58:13.9117572Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:13.9117968Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T04:58:13.9118250Z         
2025-03-14T04:58:13.9118753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:13.9119395Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:13.9119729Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T04:58:13.9120146Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:13.9120510Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T04:58:13.9120857Z         
2025-03-14T04:58:13.9121299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:13.9122026Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:13.9122433Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:13.9122795Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T04:58:13.9123125Z         
2025-03-14T04:58:13.9123568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:13.9124177Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:13.9124583Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:13.9124989Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T04:58:13.9125256Z         
2025-03-14T04:58:13.9125757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:13.9126237Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:13.9126510Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:13.9126753Z         
2025-03-14T04:58:13.9127231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:13.9127708Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:13.9127979Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:13.9128221Z         
2025-03-14T04:58:13.9128623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:13.9129172Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:13.9129487Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:13.9129742Z         
2025-03-14T04:58:13.9130160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:13.9130665Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:13.9131012Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:13.9131272Z         
2025-03-14T04:58:13.9131730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:13.9132335Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:13.9132644Z         
2025-03-14T04:58:13.9133167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:13.9133750Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T04:58:13.9134047Z         
2025-03-14T04:58:13.9134530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:13.9135174Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T04:58:13.9135477Z         
2025-03-14T04:58:13.9135982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:13.9136678Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T04:58:13.9137011Z         
2025-03-14T04:58:13.9137553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:13.9138281Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T04:58:13.9138704Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T04:58:13.9139067Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T04:58:13.9139358Z         
2025-03-14T04:58:13.9139898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:13.9140527Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T04:58:13.9140835Z         
2025-03-14T04:58:13.9141256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:13.9141796Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T04:58:13.9142081Z         
2025-03-14T04:58:13.9142510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:13.9143046Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T04:58:13.9143377Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9143735Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T04:58:13.9144025Z         
2025-03-14T04:58:13.9144583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:13.9145178Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T04:58:13.9145541Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T04:58:13.9145920Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T04:58:13.9146209Z         
2025-03-14T04:58:13.9146635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:13.9147158Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9147446Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T04:58:13.9147737Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T04:58:13.9148006Z         
2025-03-14T04:58:13.9148430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:13.9148985Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T04:58:13.9149305Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T04:58:13.9149602Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T04:58:13.9149874Z         
2025-03-14T04:58:13.9150307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:13.9150855Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:13.9151206Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T04:58:13.9151481Z         
2025-03-14T04:58:13.9151898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:13.9152455Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:13.9152800Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T04:58:13.9153051Z         
2025-03-14T04:58:13.9153550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:13.9154115Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:13.9154459Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T04:58:13.9154710Z         
2025-03-14T04:58:13.9155123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:13.9155691Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T04:58:13.9156063Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T04:58:13.9156303Z         
2025-03-14T04:58:13.9156750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:13.9157310Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T04:58:13.9157588Z         
2025-03-14T04:58:13.9158049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:13.9158609Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T04:58:13.9158882Z         
2025-03-14T04:58:13.9159360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:13.9159953Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T04:58:13.9160293Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T04:58:13.9160653Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T04:58:13.9161028Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T04:58:13.9161313Z         
2025-03-14T04:58:13.9161774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:13.9162341Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T04:58:13.9162684Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T04:58:13.9163037Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T04:58:13.9163409Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T04:58:13.9163688Z         
2025-03-14T04:58:13.9164137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:13.9164669Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T04:58:13.9165012Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T04:58:13.9165377Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T04:58:13.9165645Z         
2025-03-14T04:58:13.9166077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:13.9166592Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T04:58:13.9166941Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T04:58:13.9167337Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T04:58:13.9167601Z         
2025-03-14T04:58:13.9168017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:13.9168493Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T04:58:13.9168774Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T04:58:13.9169031Z         
2025-03-14T04:58:13.9169450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:13.9169933Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T04:58:13.9170211Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T04:58:13.9170463Z         
2025-03-14T04:58:13.9170880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:13.9171386Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T04:58:13.9171743Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T04:58:13.9172018Z         
2025-03-14T04:58:13.9172451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:13.9172968Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T04:58:13.9173291Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T04:58:13.9173562Z         
2025-03-14T04:58:13.9174007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:13.9174647Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T04:58:13.9174960Z         
2025-03-14T04:58:13.9175401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:13.9175983Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T04:58:13.9176290Z         
2025-03-14T04:58:13.9176785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:13.9177428Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T04:58:13.9177739Z         
2025-03-14T04:58:13.9178239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:13.9178930Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T04:58:13.9179277Z         
2025-03-14T04:58:13.9180022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:13.9180738Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T04:58:13.9181160Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T04:58:13.9181814Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T04:58:13.9182101Z         
2025-03-14T04:58:13.9182598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:13.9183244Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T04:58:13.9183573Z         
2025-03-14T04:58:13.9184050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:13.9184662Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T04:58:13.9184954Z         
2025-03-14T04:58:13.9185380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:13.9185935Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T04:58:13.9186284Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9186743Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T04:58:13.9187049Z         
2025-03-14T04:58:13.9187544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:13.9188113Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T04:58:13.9188434Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T04:58:13.9188786Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T04:58:13.9189074Z         
2025-03-14T04:58:13.9189502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:13.9190026Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9190320Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T04:58:13.9190615Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T04:58:13.9190886Z         
2025-03-14T04:58:13.9191308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:13.9191873Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T04:58:13.9192207Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T04:58:13.9192521Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T04:58:13.9192801Z         
2025-03-14T04:58:13.9193248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:13.9193840Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:13.9194207Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T04:58:13.9194473Z         
2025-03-14T04:58:13.9194884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:13.9195420Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:13.9195761Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T04:58:13.9196054Z         
2025-03-14T04:58:13.9196459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:13.9196996Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:13.9197336Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T04:58:13.9197584Z         
2025-03-14T04:58:13.9197997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:13.9198567Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T04:58:13.9198938Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T04:58:13.9199181Z         
2025-03-14T04:58:13.9199630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:13.9200196Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T04:58:13.9200477Z         
2025-03-14T04:58:13.9200948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:13.9201524Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T04:58:13.9201825Z         
2025-03-14T04:58:13.9202282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:13.9202850Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T04:58:13.9203194Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T04:58:13.9203549Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T04:58:13.9203923Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T04:58:13.9204204Z         
2025-03-14T04:58:13.9204656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:13.9205220Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T04:58:13.9205557Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T04:58:13.9205908Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T04:58:13.9206278Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T04:58:13.9206554Z         
2025-03-14T04:58:13.9207122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:13.9207711Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T04:58:13.9208073Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T04:58:13.9208469Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T04:58:13.9208754Z         
2025-03-14T04:58:13.9209265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:13.9209842Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T04:58:13.9210249Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T04:58:13.9210623Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T04:58:13.9210894Z         
2025-03-14T04:58:13.9211317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:13.9211816Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T04:58:13.9212099Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T04:58:13.9212355Z         
2025-03-14T04:58:13.9212774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:13.9213261Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T04:58:13.9213542Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T04:58:13.9213794Z         
2025-03-14T04:58:13.9214206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:13.9214740Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T04:58:13.9215068Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T04:58:13.9215339Z         
2025-03-14T04:58:13.9215767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:13.9216290Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T04:58:13.9216610Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T04:58:13.9216887Z         
2025-03-14T04:58:13.9217335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:13.9217967Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T04:58:13.9218282Z         
2025-03-14T04:58:13.9218725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:13.9219308Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T04:58:13.9219612Z         
2025-03-14T04:58:13.9220108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:13.9220749Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T04:58:13.9221058Z         
2025-03-14T04:58:13.9221573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:13.9222268Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T04:58:13.9222613Z         
2025-03-14T04:58:13.9223176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:13.9223878Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T04:58:13.9224439Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T04:58:13.9224840Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T04:58:13.9225134Z         
2025-03-14T04:58:13.9225653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:13.9226322Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T04:58:13.9226653Z         
2025-03-14T04:58:13.9227100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:13.9227677Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T04:58:13.9227982Z         
2025-03-14T04:58:13.9228431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:13.9228985Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T04:58:13.9229362Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9229737Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T04:58:13.9230042Z         
2025-03-14T04:58:13.9230514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:13.9231083Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T04:58:13.9231455Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T04:58:13.9231976Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T04:58:13.9232291Z         
2025-03-14T04:58:13.9232883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:13.9233434Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9233740Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T04:58:13.9234055Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T04:58:13.9234347Z         
2025-03-14T04:58:13.9234797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:13.9235378Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T04:58:13.9235715Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T04:58:13.9236025Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T04:58:13.9236309Z         
2025-03-14T04:58:13.9236757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:13.9237327Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:13.9237697Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T04:58:13.9237966Z         
2025-03-14T04:58:13.9238394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:13.9238926Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:13.9239294Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T04:58:13.9239542Z         
2025-03-14T04:58:13.9239946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:13.9240476Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:13.9240814Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T04:58:13.9241061Z         
2025-03-14T04:58:13.9241474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:13.9242038Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T04:58:13.9242401Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T04:58:13.9242643Z         
2025-03-14T04:58:13.9243089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:13.9243651Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T04:58:13.9243959Z         
2025-03-14T04:58:13.9244407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:13.9244981Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T04:58:13.9245274Z         
2025-03-14T04:58:13.9245730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:13.9246294Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T04:58:13.9246638Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T04:58:13.9246997Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T04:58:13.9247369Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T04:58:13.9247649Z         
2025-03-14T04:58:13.9248111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:13.9248682Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T04:58:13.9249013Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T04:58:13.9249360Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T04:58:13.9249728Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T04:58:13.9250004Z         
2025-03-14T04:58:13.9250449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:13.9250979Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T04:58:13.9251328Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T04:58:13.9251700Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T04:58:13.9251972Z         
2025-03-14T04:58:13.9252424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:13.9252975Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T04:58:13.9253335Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T04:58:13.9253710Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T04:58:13.9253985Z         
2025-03-14T04:58:13.9254412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:13.9254907Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T04:58:13.9255197Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T04:58:13.9255452Z         
2025-03-14T04:58:13.9255867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:13.9256358Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T04:58:13.9256640Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T04:58:13.9256893Z         
2025-03-14T04:58:13.9257327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:13.9257832Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T04:58:13.9259462Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T04:58:13.9259771Z         
2025-03-14T04:58:13.9260180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:13.9260673Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T04:58:13.9260988Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T04:58:13.9261244Z         
2025-03-14T04:58:13.9261692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:13.9262308Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T04:58:13.9262624Z         
2025-03-14T04:58:13.9263054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:13.9263623Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T04:58:13.9263919Z         
2025-03-14T04:58:13.9264557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:13.9265241Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T04:58:13.9265567Z         
2025-03-14T04:58:13.9266064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T04:58:13.9266738Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T04:58:13.9267074Z         
2025-03-14T04:58:13.9267602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T04:58:13.9268313Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T04:58:13.9268714Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T04:58:13.9269066Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T04:58:13.9269332Z         
2025-03-14T04:58:13.9269817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:13.9270427Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T04:58:13.9270724Z         
2025-03-14T04:58:13.9271133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:13.9271657Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T04:58:13.9271925Z         
2025-03-14T04:58:13.9272335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:13.9272875Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T04:58:13.9273194Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9273552Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T04:58:13.9274016Z         
2025-03-14T04:58:13.9274440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:13.9274960Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T04:58:13.9275278Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T04:58:13.9275619Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T04:58:13.9275899Z         
2025-03-14T04:58:13.9276314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:13.9276824Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T04:58:13.9277102Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T04:58:13.9277385Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T04:58:13.9277647Z         
2025-03-14T04:58:13.9278060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:13.9278588Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T04:58:13.9278892Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T04:58:13.9279173Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T04:58:13.9279429Z         
2025-03-14T04:58:13.9279838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:13.9280360Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:13.9280697Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T04:58:13.9280941Z         
2025-03-14T04:58:13.9281338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:13.9282180Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:13.9282587Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T04:58:13.9282832Z         
2025-03-14T04:58:13.9283232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:13.9283748Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:13.9284077Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T04:58:13.9284312Z         
2025-03-14T04:58:13.9284717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:13.9285265Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T04:58:13.9285626Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T04:58:13.9285864Z         
2025-03-14T04:58:13.9286304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:13.9286869Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T04:58:13.9287131Z         
2025-03-14T04:58:13.9287569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:13.9288123Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T04:58:13.9288383Z         
2025-03-14T04:58:13.9288815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:13.9289355Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T04:58:13.9289678Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T04:58:13.9290017Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T04:58:13.9290367Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T04:58:13.9290627Z         
2025-03-14T04:58:13.9291069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:13.9291614Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T04:58:13.9291933Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T04:58:13.9292265Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T04:58:13.9292609Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T04:58:13.9292870Z         
2025-03-14T04:58:13.9293295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:13.9293800Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T04:58:13.9294127Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T04:58:13.9294498Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T04:58:13.9294760Z         
2025-03-14T04:58:13.9295197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:13.9295748Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T04:58:13.9296086Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T04:58:13.9296445Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T04:58:13.9296716Z         
2025-03-14T04:58:13.9297121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:13.9297595Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T04:58:13.9297861Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T04:58:13.9298100Z         
2025-03-14T04:58:13.9298494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:13.9298954Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T04:58:13.9299212Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T04:58:13.9299446Z         
2025-03-14T04:58:13.9299899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:13.9300382Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T04:58:13.9300704Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T04:58:13.9300983Z         
2025-03-14T04:58:13.9301380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:13.9301875Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T04:58:13.9302176Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T04:58:13.9302435Z         
2025-03-14T04:58:13.9302882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:13.9303488Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T04:58:13.9303798Z         
2025-03-14T04:58:13.9304309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:13.9304890Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T04:58:13.9305211Z         
2025-03-14T04:58:13.9305746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T04:58:13.9306425Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T04:58:13.9306738Z         
2025-03-14T04:58:13.9307356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T04:58:13.9308103Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T04:58:13.9308371Z         
2025-03-14T04:58:13.9308774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9309286Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T04:58:13.9309571Z         
2025-03-14T04:58:13.9310112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:13.9310792Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T04:58:13.9311139Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T04:58:13.9311421Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T04:58:13.9311657Z         
2025-03-14T04:58:13.9312223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:13.9312886Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:13.9313317Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T04:58:13.9313672Z         
2025-03-14T04:58:13.9314261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:13.9314969Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:13.9315284Z         
2025-03-14T04:58:13.9315679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9316166Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T04:58:13.9316416Z         
2025-03-14T04:58:13.9316954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:13.9317630Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T04:58:13.9317968Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T04:58:13.9318250Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T04:58:13.9318494Z         
2025-03-14T04:58:13.9319037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:13.9319672Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:13.9320098Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T04:58:13.9320447Z         
2025-03-14T04:58:13.9320985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:13.9321650Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:13.9321937Z         
2025-03-14T04:58:13.9322320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9322798Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T04:58:13.9323043Z         
2025-03-14T04:58:13.9323557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:13.9324228Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T04:58:13.9324568Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T04:58:13.9324849Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T04:58:13.9325091Z         
2025-03-14T04:58:13.9325634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:13.9326275Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:13.9326698Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T04:58:13.9327051Z         
2025-03-14T04:58:13.9327612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:13.9328299Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:13.9328592Z         
2025-03-14T04:58:13.9329004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9329514Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T04:58:13.9329772Z         
2025-03-14T04:58:13.9330305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:13.9330963Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T04:58:13.9331301Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T04:58:13.9331579Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T04:58:13.9331819Z         
2025-03-14T04:58:13.9332365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:13.9333009Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T04:58:13.9333431Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T04:58:13.9333782Z         
2025-03-14T04:58:13.9334323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:13.9334998Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:13.9335288Z         
2025-03-14T04:58:13.9335677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9336161Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T04:58:13.9336400Z         
2025-03-14T04:58:13.9336918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T04:58:13.9337595Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T04:58:13.9337934Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T04:58:13.9338214Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T04:58:13.9338453Z         
2025-03-14T04:58:13.9339000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T04:58:13.9339670Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T04:58:13.9340114Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T04:58:13.9340468Z         
2025-03-14T04:58:13.9341003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T04:58:13.9341687Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T04:58:13.9341975Z         
2025-03-14T04:58:13.9342378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T04:58:13.9342894Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T04:58:13.9343145Z         
2025-03-14T04:58:13.9343514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:13.9344317Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T04:58:13.9344815Z         
2025-03-14T04:58:13.9345205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:13.9346045Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T04:58:13.9346640Z         
2025-03-14T04:58:13.9347022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:13.9347582Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T04:58:13.9347904Z         
2025-03-14T04:58:13.9348425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T04:58:13.9349065Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T04:58:13.9349352Z         
2025-03-14T04:58:13.9349773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T04:58:13.9350313Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T04:58:13.9350601Z         
2025-03-14T04:58:13.9351101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T04:58:13.9351742Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T04:58:13.9352017Z         
2025-03-14T04:58:13.9352642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T04:58:13.9353369Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T04:58:13.9353708Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:13.9354075Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T04:58:13.9354436Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T04:58:13.9354696Z         
2025-03-14T04:58:13.9355153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T04:58:13.9355697Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T04:58:13.9355935Z         
2025-03-14T04:58:41.2278865Z 
2025-03-14T04:58:41.2285160Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:41.2348420Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:41.2350856Z         l_stack0_ = L_stack0_
2025-03-14T04:58:41.2352968Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:58:41.2353604Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:58:41.2354201Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:58:41.2354828Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:58:41.2356315Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:58:41.2359099Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:58:41.2359784Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:58:41.2362896Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:58:41.2363493Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2363915Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2364587Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2365001Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2365313Z         
2025-03-14T04:58:41.2365763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:58:41.2366282Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:58:41.2366997Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:58:41.2368058Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:58:41.2368855Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:58:41.2369599Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:58:41.2369888Z         
2025-03-14T04:58:41.2370354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:58:41.2371388Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:58:41.2372125Z         
2025-03-14T04:58:41.2372553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:58:41.2373585Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:58:41.2374346Z         
2025-03-14T04:58:41.2374740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2375220Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2375485Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:41.2375730Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:41.2376018Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2376272Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:41.2376525Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:41.2376811Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2377072Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:41.2377321Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:41.2377599Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2377855Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:41.2378092Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:41.2378319Z         
2025-03-14T04:58:41.2378737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:41.2379525Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:41.2380087Z         
2025-03-14T04:58:41.2380559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:41.2381159Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:58:41.2381690Z         
2025-03-14T04:58:41.2382128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:41.2382686Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:41.2382989Z         
2025-03-14T04:58:41.2383417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:41.2384016Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:41.2384488Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2384880Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:41.2385238Z         
2025-03-14T04:58:41.2385675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:41.2386241Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:41.2386597Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:41.2386978Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:41.2387271Z         
2025-03-14T04:58:41.2387693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:41.2388223Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2388541Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:41.2388849Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:41.2389125Z         
2025-03-14T04:58:41.2389567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:41.2390142Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:41.2390482Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:41.2390802Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:41.2391084Z         
2025-03-14T04:58:41.2391552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:41.2392125Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:41.2392488Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:41.2392748Z         
2025-03-14T04:58:41.2393186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:41.2393791Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:41.2394142Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:41.2394406Z         
2025-03-14T04:58:41.2394842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:41.2395407Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:41.2395769Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:41.2396044Z         
2025-03-14T04:58:41.2396452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:41.2397021Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:41.2397391Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:41.2397638Z         
2025-03-14T04:58:41.2398108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:41.2398672Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:41.2398962Z         
2025-03-14T04:58:41.2399419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:41.2400001Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:41.2400272Z         
2025-03-14T04:58:41.2400731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:41.2401309Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:41.2401648Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:41.2402005Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:41.2402373Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:41.2402649Z         
2025-03-14T04:58:41.2403116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:41.2403694Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:41.2404036Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:41.2404388Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:41.2404754Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:41.2405028Z         
2025-03-14T04:58:41.2405480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:41.2406020Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:41.2406369Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:41.2406732Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:41.2407069Z         
2025-03-14T04:58:41.2407521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:41.2408057Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:41.2408419Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:41.2408799Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:41.2409071Z         
2025-03-14T04:58:41.2409503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:41.2410003Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:41.2410287Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:41.2410542Z         
2025-03-14T04:58:41.2410963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:41.2411460Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:41.2411764Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:41.2412017Z         
2025-03-14T04:58:41.2412448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:41.2412980Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:41.2413280Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:41.2413549Z         
2025-03-14T04:58:41.2413966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:41.2414483Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:41.2414809Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:41.2415086Z         
2025-03-14T04:58:41.2415562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:41.2416183Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:41.2416504Z         
2025-03-14T04:58:41.2416972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:41.2417598Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:41.2417922Z         
2025-03-14T04:58:41.2418417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:41.2419187Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:41.2419871Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:41.2420208Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:41.2420671Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:41.2421028Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:41.2421315Z         
2025-03-14T04:58:41.2421791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2422422Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2422817Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:41.2423095Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:41.2423513Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2423899Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:41.2424167Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:41.2424693Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2425139Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:41.2425523Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:41.2425950Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2426478Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:41.2426743Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:41.2427083Z         
2025-03-14T04:58:41.2427637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:41.2428439Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:58:41.2428921Z         
2025-03-14T04:58:41.2429501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:41.2430342Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:41.2430820Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:41.2431224Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:41.2431558Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:41.2431967Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:41.2432281Z         
2025-03-14T04:58:41.2432996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:41.2433876Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:41.2434330Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:41.2434712Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:41.2435169Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:41.2435494Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:41.2435819Z         
2025-03-14T04:58:41.2436319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:41.2436999Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:41.2437295Z         
2025-03-14T04:58:41.2471717Z 
2025-03-14T04:58:41.2472076Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:41.2474309Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:41.2476750Z         l_stack0_ = L_stack0_
2025-03-14T04:58:41.2477134Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:58:41.2477660Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:58:41.2478214Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:58:41.2478734Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:58:41.2479327Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:58:41.2479986Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:58:41.2480612Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:58:41.2481250Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:58:41.2481974Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2482435Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2482882Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2483335Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2483745Z         
2025-03-14T04:58:41.2484165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:58:41.2484692Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:58:41.2485470Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:58:41.2486266Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:58:41.2487067Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:58:41.2487856Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:58:41.2488270Z         
2025-03-14T04:58:41.2488711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:58:41.2489746Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:58:41.2490539Z         
2025-03-14T04:58:41.2490999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:58:41.2492063Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:58:41.2492835Z         
2025-03-14T04:58:41.2493226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2493748Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2494019Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:41.2494277Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:41.2494605Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2495085Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:41.2495342Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:41.2495638Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2495904Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:41.2496160Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:41.2496445Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2496709Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:41.2497032Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:41.2497264Z         
2025-03-14T04:58:41.2497660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:41.2498479Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:41.2499069Z         
2025-03-14T04:58:41.2499577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:41.2500224Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:58:41.2500525Z         
2025-03-14T04:58:41.2500968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:41.2501558Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:41.2501870Z         
2025-03-14T04:58:41.2502309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:41.2502840Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:41.2503180Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2503562Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:41.2503848Z         
2025-03-14T04:58:41.2504412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:41.2505018Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:41.2505386Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:41.2505779Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:41.2506103Z         
2025-03-14T04:58:41.2506565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:41.2507145Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2507472Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:41.2507792Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:41.2508080Z         
2025-03-14T04:58:41.2508569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:41.2509242Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:41.2509671Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:41.2509998Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:41.2510291Z         
2025-03-14T04:58:41.2510776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:41.2511368Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:41.2511743Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:41.2512009Z         
2025-03-14T04:58:41.2512458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:41.2513040Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:41.2513404Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:41.2513653Z         
2025-03-14T04:58:41.2514063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:41.2514599Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:41.2514940Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:41.2515189Z         
2025-03-14T04:58:41.2515604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:41.2516165Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:41.2516531Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:41.2516781Z         
2025-03-14T04:58:41.2517234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:41.2517799Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:41.2518106Z         
2025-03-14T04:58:41.2518560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:41.2519155Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:41.2519439Z         
2025-03-14T04:58:41.2519929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:41.2520543Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:41.2520905Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:41.2521286Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:41.2521660Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:41.2522017Z         
2025-03-14T04:58:41.2522528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:41.2523242Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:41.2523702Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:41.2524082Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:41.2524468Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:41.2524745Z         
2025-03-14T04:58:41.2525191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:41.2525731Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:41.2526077Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:41.2526444Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:41.2526708Z         
2025-03-14T04:58:41.2527157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:41.2527683Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:41.2528034Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:41.2528405Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:41.2528671Z         
2025-03-14T04:58:41.2529101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:41.2529584Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:41.2529865Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:41.2530119Z         
2025-03-14T04:58:41.2530543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:41.2531029Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:41.2531309Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:41.2531559Z         
2025-03-14T04:58:41.2531973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:41.2532501Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:41.2532812Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:41.2533080Z         
2025-03-14T04:58:41.2533496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:41.2533996Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:41.2534305Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:41.2534563Z         
2025-03-14T04:58:41.2535018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:41.2535627Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:41.2535934Z         
2025-03-14T04:58:41.2536383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:41.2537013Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:41.2537324Z         
2025-03-14T04:58:41.2537817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:41.2538663Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:41.2539132Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:41.2539441Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:41.2539765Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:41.2540102Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:41.2540401Z         
2025-03-14T04:58:41.2540844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2541486Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2541883Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:41.2542160Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:41.2542587Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2542986Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:41.2543257Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:41.2543665Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2544056Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:41.2544425Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:41.2544865Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2545255Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:41.2545536Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:41.2545786Z         
2025-03-14T04:58:41.2546259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:41.2546931Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:58:41.2547260Z         
2025-03-14T04:58:41.2547788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:41.2548547Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:41.2549015Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:41.2549341Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:41.2549678Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:41.2550028Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:41.2550317Z         
2025-03-14T04:58:41.2550956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:41.2551835Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:41.2552335Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:41.2552709Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:41.2553144Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:41.2553488Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:41.2553765Z         
2025-03-14T04:58:41.2554271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:41.2554867Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:41.2555135Z         
2025-03-14T04:58:41.2555241Z 
2025-03-14T04:58:41.2555344Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:41.2557450Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:41.2559716Z         l_stack0_ = L_stack0_
2025-03-14T04:58:41.2560096Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:58:41.2560632Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:58:41.2561153Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:58:41.2561671Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:58:41.2562275Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:58:41.2562901Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:58:41.2563533Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:58:41.2564156Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:58:41.2564690Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2565141Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2565561Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2566003Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2566329Z         
2025-03-14T04:58:41.2566742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:58:41.2567297Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:58:41.2568099Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:58:41.2568898Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:58:41.2569661Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:58:41.2570416Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:58:41.2570729Z         
2025-03-14T04:58:41.2571183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:58:41.2572260Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:58:41.2573055Z         
2025-03-14T04:58:41.2573502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:58:41.2574585Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:58:41.2575596Z         
2025-03-14T04:58:41.2576140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2576638Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2576914Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:41.2577212Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:41.2577533Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2577807Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:41.2578063Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:41.2578353Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2578621Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:41.2578883Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:41.2579183Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2579455Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:41.2579714Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:41.2579957Z         
2025-03-14T04:58:41.2580374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:41.2581235Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:41.2582053Z         
2025-03-14T04:58:41.2582838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:41.2583556Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:58:41.2583915Z         
2025-03-14T04:58:41.2584456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:41.2585080Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:41.2585416Z         
2025-03-14T04:58:41.2585870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:41.2586440Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:41.2586801Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2587174Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:41.2587468Z         
2025-03-14T04:58:41.2587927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:41.2588496Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:41.2588846Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:41.2589225Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:41.2589533Z         
2025-03-14T04:58:41.2589987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:41.2590545Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2590860Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:41.2591174Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:41.2591454Z         
2025-03-14T04:58:41.2591900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:41.2592512Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:41.2592855Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:41.2593256Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:41.2593565Z         
2025-03-14T04:58:41.2594031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:41.2594613Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:41.2594985Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:41.2595252Z         
2025-03-14T04:58:41.2595688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:41.2596257Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:41.2596619Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:41.2596870Z         
2025-03-14T04:58:41.2597324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:41.2597884Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:41.2598243Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:41.2598512Z         
2025-03-14T04:58:41.2598931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:41.2599477Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:41.2599836Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:41.2600077Z         
2025-03-14T04:58:41.2600510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:41.2601055Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:41.2601319Z         
2025-03-14T04:58:41.2601748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:41.2602282Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:41.2602545Z         
2025-03-14T04:58:41.2602990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:41.2603551Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:41.2603884Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:41.2604230Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:41.2604588Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:41.2604857Z         
2025-03-14T04:58:41.2605310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:41.2605907Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:41.2606238Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:41.2606613Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:41.2606972Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:41.2607239Z         
2025-03-14T04:58:41.2607679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:41.2608203Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:41.2608546Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:41.2608908Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:41.2609164Z         
2025-03-14T04:58:41.2609602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:41.2610119Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:41.2610474Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:41.2610886Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:41.2611152Z         
2025-03-14T04:58:41.2611587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:41.2612094Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:41.2612372Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:41.2612618Z         
2025-03-14T04:58:41.2613032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:41.2613511Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:41.2613781Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:41.2614025Z         
2025-03-14T04:58:41.2614436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:41.2614931Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:41.2615235Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:41.2615488Z         
2025-03-14T04:58:41.2615892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:41.2616381Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:41.2616682Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:41.2616933Z         
2025-03-14T04:58:41.2617384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:41.2618005Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:41.2618310Z         
2025-03-14T04:58:41.2618748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:41.2619321Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:41.2619653Z         
2025-03-14T04:58:41.2620112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:41.2620804Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:41.2621251Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:41.2621544Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:41.2621852Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:41.2622164Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:41.2622424Z         
2025-03-14T04:58:41.2622816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2623388Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2623745Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:41.2623995Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:41.2624519Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2624913Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:41.2625175Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:41.2625613Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2625987Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:41.2626229Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:41.2626599Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2626955Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:41.2627192Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:41.2627412Z         
2025-03-14T04:58:41.2627839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:41.2628394Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:58:41.2628691Z         
2025-03-14T04:58:41.2629145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:41.2629828Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:41.2630258Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:41.2630553Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:41.2630855Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:41.2631170Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:41.2631430Z         
2025-03-14T04:58:41.2631996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:41.2632793Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:41.2633136Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:41.2633473Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:41.2633855Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:41.2634150Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:41.2634398Z         
2025-03-14T04:58:41.2634860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:41.2635399Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:41.2635645Z         
2025-03-14T04:58:41.2635833Z 
2025-03-14T04:58:41.2635932Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:41.2637969Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:41.2640053Z         l_stack0_ = L_stack0_
2025-03-14T04:58:41.2640412Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T04:58:41.2640914Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T04:58:41.2641407Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T04:58:41.2641900Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T04:58:41.2642439Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T04:58:41.2643029Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T04:58:41.2643617Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T04:58:41.2644200Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T04:58:41.2644705Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2645125Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2645540Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2645955Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:41.2646265Z         
2025-03-14T04:58:41.2646664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T04:58:41.2647154Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T04:58:41.2647890Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T04:58:41.2648682Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T04:58:41.2649436Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T04:58:41.2650185Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T04:58:41.2650483Z         
2025-03-14T04:58:41.2650903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T04:58:41.2651942Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T04:58:41.2652689Z         
2025-03-14T04:58:41.2653146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T04:58:41.2654220Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T04:58:41.2655002Z         
2025-03-14T04:58:41.2655418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2655936Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2656207Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:41.2656454Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:41.2656747Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2657017Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:41.2657273Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:41.2657572Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2657850Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:41.2658113Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:41.2658413Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:41.2658684Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:41.2658942Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:41.2659182Z         
2025-03-14T04:58:41.2659599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:41.2660459Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:41.2661075Z         
2025-03-14T04:58:41.2661588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:41.2662290Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T04:58:41.2662599Z         
2025-03-14T04:58:41.2663043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:41.2663635Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:41.2663954Z         
2025-03-14T04:58:41.2664478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:41.2665067Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:41.2665423Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2665806Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:41.2666115Z         
2025-03-14T04:58:41.2666584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:41.2667148Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:41.2667578Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:41.2667954Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:41.2668278Z         
2025-03-14T04:58:41.2668743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:41.2669289Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:41.2669607Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:41.2669913Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:41.2670189Z         
2025-03-14T04:58:41.2670637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:41.2671199Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:41.2671518Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:41.2671821Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:41.2672085Z         
2025-03-14T04:58:41.2672523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:41.2673090Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:41.2673449Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:41.2673698Z         
2025-03-14T04:58:41.2674110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:41.2674636Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:41.2674974Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:41.2675221Z         
2025-03-14T04:58:41.2675631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:41.2676155Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:41.2676523Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:41.2676770Z         
2025-03-14T04:58:41.2677185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:41.2677749Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:41.2678111Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:41.2678354Z         
2025-03-14T04:58:41.2678806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:41.2679370Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:41.2679641Z         
2025-03-14T04:58:41.2680079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:41.2680643Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:41.2680920Z         
2025-03-14T04:58:41.2681647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:41.2682330Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:41.2682694Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:41.2683047Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:41.2683415Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:41.2683691Z         
2025-03-14T04:58:41.2684150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:41.2684725Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:41.2685066Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:41.2685418Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:41.2685778Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:41.2686051Z         
2025-03-14T04:58:41.2686494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:41.2687030Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:41.2687373Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:41.2687738Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:41.2688007Z         
2025-03-14T04:58:41.2688452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:41.2688982Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:41.2689343Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:41.2689712Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:41.2689982Z         
2025-03-14T04:58:41.2690449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:41.2690940Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:41.2691225Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:41.2691476Z         
2025-03-14T04:58:41.2691897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:41.2692383Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:41.2692658Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:41.2692904Z         
2025-03-14T04:58:41.2693340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:41.2693843Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:41.2694151Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:41.2694413Z         
2025-03-14T04:58:41.2694863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:41.2695362Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:41.2695686Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:41.2695946Z         
2025-03-14T04:58:41.2696424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:41.2697036Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:41.2697352Z         
2025-03-14T04:58:41.2697796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:41.2698373Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:41.2698675Z         
2025-03-14T04:58:41.2699150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:41.2699862Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:41.2700308Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:41.2700611Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:41.2700924Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:41.2701246Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:41.2701510Z         
2025-03-14T04:58:41.2701910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:41.2702495Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2702861Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:41.2703115Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:41.2703492Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2703854Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:41.2704106Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:41.2704592Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2704976Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:41.2705242Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:41.2705648Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:41.2706028Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:41.2706275Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:41.2706510Z         
2025-03-14T04:58:41.2706971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:41.2707558Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T04:58:41.2707877Z         
2025-03-14T04:58:41.2708369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:41.2709134Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:41.2709598Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:41.2709921Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:41.2710266Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:41.2710641Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:41.2710919Z         
2025-03-14T04:58:41.2711552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:41.2712344Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:41.2712730Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:41.2713101Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:41.2713473Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:41.2713797Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:41.2714066Z         
2025-03-14T04:58:41.2714533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:41.2715066Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:41.2715312Z         
2025-03-14T04:58:43.0742857Z 
2025-03-14T04:58:43.0748001Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:43.0751780Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:43.0752937Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:58:43.0753216Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:58:43.0753578Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0754038Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0754830Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0755272Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0755609Z         
2025-03-14T04:58:43.0756088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0756618Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0756891Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:43.0757154Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:43.0757469Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0757747Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:43.0758016Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:43.0758325Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0758604Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:43.0758865Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:43.0759164Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0759438Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:43.0759759Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:43.0760016Z         
2025-03-14T04:58:43.0760493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:43.0761425Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:43.0762034Z         
2025-03-14T04:58:43.0762560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:43.0763203Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:58:43.0763512Z         
2025-03-14T04:58:43.0763966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:43.0764577Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:43.0764868Z         
2025-03-14T04:58:43.0765285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:43.0765805Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:43.0766130Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0766475Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:43.0766757Z         
2025-03-14T04:58:43.0767195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:43.0767729Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:43.0768062Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:43.0768421Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:43.0768712Z         
2025-03-14T04:58:43.0769136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:43.0769686Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0769988Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:43.0770286Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:43.0770551Z         
2025-03-14T04:58:43.0770972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:43.0771518Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:43.0771852Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:43.0772154Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:43.0772422Z         
2025-03-14T04:58:43.0772861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:43.0773399Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:43.0773771Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:43.0774025Z         
2025-03-14T04:58:43.0774473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:43.0775009Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:43.0775364Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:43.0775598Z         
2025-03-14T04:58:43.0775996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:43.0776519Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:43.0776856Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:43.0777108Z         
2025-03-14T04:58:43.0777532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:43.0778108Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:43.0778491Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:43.0778740Z         
2025-03-14T04:58:43.0779191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:43.0779759Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:43.0780040Z         
2025-03-14T04:58:43.0780486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:43.0781045Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:43.0781326Z         
2025-03-14T04:58:43.0782130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:43.0782705Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:43.0783047Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:43.0783445Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:43.0783817Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:43.0784093Z         
2025-03-14T04:58:43.0784683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:43.0785295Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:43.0785654Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:43.0786023Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:43.0786405Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:43.0786684Z         
2025-03-14T04:58:43.0787131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:43.0787669Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:43.0788053Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:43.0788421Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:43.0788691Z         
2025-03-14T04:58:43.0789185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:43.0789787Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:43.0790166Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:43.0790540Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:43.0790811Z         
2025-03-14T04:58:43.0791237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:43.0791724Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:43.0792004Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:43.0792285Z         
2025-03-14T04:58:43.0792709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:43.0793195Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:43.0793471Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:43.0793716Z         
2025-03-14T04:58:43.0794132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:43.0794631Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:43.0794941Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:43.0795208Z         
2025-03-14T04:58:43.0795619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:43.0796119Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:43.0796425Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:43.0796682Z         
2025-03-14T04:58:43.0797145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:43.0797777Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:43.0798084Z         
2025-03-14T04:58:43.0798512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:43.0799080Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:43.0799377Z         
2025-03-14T04:58:43.0799832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:43.0800517Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:43.0800948Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:43.0801244Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:43.0801545Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:43.0801874Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:43.0802143Z         
2025-03-14T04:58:43.0802563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0803155Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0803503Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:43.0803747Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:43.0804113Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0804459Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:43.0804699Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:43.0805071Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0805423Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:43.0805670Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:43.0806039Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0806388Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:43.0806627Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:43.0806863Z         
2025-03-14T04:58:43.0807285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:43.0807884Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:58:43.0808219Z         
2025-03-14T04:58:43.0808675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:43.0809362Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:43.0809789Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:43.0810084Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:43.0810396Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:43.0811853Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:43.0812119Z         
2025-03-14T04:58:43.0812674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:43.0813380Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:43.0813721Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:43.0814053Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:43.0814381Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:43.0814677Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:43.0814923Z         
2025-03-14T04:58:43.0815389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:43.0815927Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:43.0816172Z         
2025-03-14T04:58:43.0826202Z 
2025-03-14T04:58:43.0831939Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:43.0837699Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:43.0839239Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:58:43.0839504Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:58:43.0839854Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0840287Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0840709Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0841127Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0841444Z         
2025-03-14T04:58:43.0841898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0842408Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0842682Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:43.0842926Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:43.0843227Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0843501Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:43.0843757Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:43.0844045Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0844307Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:43.0844558Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:43.0844843Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0845104Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:43.0845348Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:43.0845582Z         
2025-03-14T04:58:43.0846017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:43.0846849Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:43.0847481Z         
2025-03-14T04:58:43.0847973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:43.0848581Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:58:43.0848874Z         
2025-03-14T04:58:43.0849302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:43.0849874Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:43.0850171Z         
2025-03-14T04:58:43.0850588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:43.0851104Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:43.0851434Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0851807Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:43.0852088Z         
2025-03-14T04:58:43.0852533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:43.0853078Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:43.0853403Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:43.0853753Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:43.0854035Z         
2025-03-14T04:58:43.0854442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:43.0854950Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0855244Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:43.0855531Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:43.0855791Z         
2025-03-14T04:58:43.0856202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:43.0856733Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:43.0857052Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:43.0857343Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:43.0857602Z         
2025-03-14T04:58:43.0858036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:43.0858555Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:43.0858897Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:43.0859144Z         
2025-03-14T04:58:43.0859544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:43.0860059Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:43.0860416Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:43.0860662Z         
2025-03-14T04:58:43.0861063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:43.0861580Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:43.0861911Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:43.0862152Z         
2025-03-14T04:58:43.0862560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:43.0863119Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:43.0863486Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:43.0863733Z         
2025-03-14T04:58:43.0864187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:43.0864939Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:43.0865224Z         
2025-03-14T04:58:43.0865719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:43.0866303Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:43.0866603Z         
2025-03-14T04:58:43.0867044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:43.0867613Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:43.0867971Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:43.0868345Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:43.0868733Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:43.0869021Z         
2025-03-14T04:58:43.0869507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:43.0870111Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:43.0870465Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:43.0870831Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:43.0871214Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:43.0871497Z         
2025-03-14T04:58:43.0871964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:43.0872519Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:43.0872888Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:43.0873266Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:43.0873547Z         
2025-03-14T04:58:43.0874009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:43.0874606Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:43.0874949Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:43.0875306Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:43.0875567Z         
2025-03-14T04:58:43.0875983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:43.0876459Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:43.0876731Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:43.0876978Z         
2025-03-14T04:58:43.0877383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:43.0877858Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:43.0878127Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:43.0878366Z         
2025-03-14T04:58:43.0878793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:43.0879279Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:43.0879596Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:43.0879899Z         
2025-03-14T04:58:43.0880300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:43.0880819Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:43.0881114Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:43.0881370Z         
2025-03-14T04:58:43.0882067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:43.0882681Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:43.0882980Z         
2025-03-14T04:58:43.0883417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:43.0883991Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:43.0884292Z         
2025-03-14T04:58:43.0884752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:43.0885456Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:43.0885899Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:43.0886205Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:43.0886507Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:43.0886819Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:43.0887078Z         
2025-03-14T04:58:43.0887476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0888028Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0888512Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:43.0888766Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:43.0889147Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0889501Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:43.0889748Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:43.0890122Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0890477Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:43.0890720Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:43.0891087Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0891448Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:43.0891687Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:43.0891910Z         
2025-03-14T04:58:43.0892335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:43.0892983Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:58:43.0893324Z         
2025-03-14T04:58:43.0893812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:43.0894532Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:43.0894961Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:43.0895259Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:43.0895565Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:43.0895874Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:43.0896141Z         
2025-03-14T04:58:43.0896703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:43.0897397Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:43.0897745Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:43.0898080Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:43.0898424Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:43.0898720Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:43.0898964Z         
2025-03-14T04:58:43.0899404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:43.0899945Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:43.0900179Z         
2025-03-14T04:58:43.0906876Z 
2025-03-14T04:58:43.0912798Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:43.0913796Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T04:58:43.0914812Z         l_predictions_0_ = L_predictions_0_
2025-03-14T04:58:43.0915062Z         l_predictions_1_ = L_predictions_1_
2025-03-14T04:58:43.0915405Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0915842Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0916269Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0916698Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T04:58:43.0917021Z         
2025-03-14T04:58:43.0917496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0918062Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0918331Z         getitem: "Sym(s0)" = size[0]
2025-03-14T04:58:43.0918582Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T04:58:43.0918889Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0919155Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T04:58:43.0919458Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T04:58:43.0919753Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0920021Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T04:58:43.0920294Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T04:58:43.0920621Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T04:58:43.0920897Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T04:58:43.0921142Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T04:58:43.0921382Z         
2025-03-14T04:58:43.0921801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T04:58:43.0922708Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T04:58:43.0923280Z         
2025-03-14T04:58:43.0923762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T04:58:43.0924366Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T04:58:43.0924653Z         
2025-03-14T04:58:43.0925068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T04:58:43.0925625Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T04:58:43.0925922Z         
2025-03-14T04:58:43.0926345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T04:58:43.0926868Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T04:58:43.0927198Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0927544Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T04:58:43.0927814Z         
2025-03-14T04:58:43.0928232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T04:58:43.0928775Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T04:58:43.0929098Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T04:58:43.0929449Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T04:58:43.0929734Z         
2025-03-14T04:58:43.0930148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T04:58:43.0930657Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T04:58:43.0930954Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T04:58:43.0931240Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T04:58:43.0931499Z         
2025-03-14T04:58:43.0931909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T04:58:43.0932448Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T04:58:43.0932767Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T04:58:43.0933083Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T04:58:43.0933342Z         
2025-03-14T04:58:43.0933786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T04:58:43.0934338Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T04:58:43.0934678Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T04:58:43.0934924Z         
2025-03-14T04:58:43.0935327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T04:58:43.0935850Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T04:58:43.0936179Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T04:58:43.0936424Z         
2025-03-14T04:58:43.0936824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T04:58:43.0937344Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T04:58:43.0937676Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T04:58:43.0937924Z         
2025-03-14T04:58:43.0938331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T04:58:43.0938889Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T04:58:43.0939252Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T04:58:43.0939495Z         
2025-03-14T04:58:43.0939937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T04:58:43.0940489Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T04:58:43.0940763Z         
2025-03-14T04:58:43.0941195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T04:58:43.0941737Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T04:58:43.0942018Z         
2025-03-14T04:58:43.0942463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T04:58:43.0943019Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T04:58:43.0943350Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T04:58:43.0943691Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T04:58:43.0944057Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T04:58:43.0944481Z         
2025-03-14T04:58:43.0944965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T04:58:43.0945630Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T04:58:43.0945976Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T04:58:43.0946333Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T04:58:43.0946727Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T04:58:43.0947005Z         
2025-03-14T04:58:43.0947518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T04:58:43.0948073Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T04:58:43.0948425Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T04:58:43.0948802Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T04:58:43.0949070Z         
2025-03-14T04:58:43.0949516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T04:58:43.0950052Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T04:58:43.0950410Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T04:58:43.0950786Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T04:58:43.0951055Z         
2025-03-14T04:58:43.0951478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T04:58:43.0951963Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T04:58:43.0952244Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T04:58:43.0952497Z         
2025-03-14T04:58:43.0952903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T04:58:43.0953383Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T04:58:43.0953658Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T04:58:43.0953907Z         
2025-03-14T04:58:43.0954326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T04:58:43.0954829Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T04:58:43.0955140Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T04:58:43.0955403Z         
2025-03-14T04:58:43.0955852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T04:58:43.0956343Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T04:58:43.0956645Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T04:58:43.0956903Z         
2025-03-14T04:58:43.0957359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T04:58:43.0957968Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T04:58:43.0958278Z         
2025-03-14T04:58:43.0958714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T04:58:43.0959291Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T04:58:43.0959595Z         
2025-03-14T04:58:43.0960064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T04:58:43.0960817Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T04:58:43.0961267Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T04:58:43.0961563Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T04:58:43.0961889Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T04:58:43.0962203Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T04:58:43.0962465Z         
2025-03-14T04:58:43.0962857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T04:58:43.0963428Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0963787Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T04:58:43.0964043Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T04:58:43.0964423Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0964779Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T04:58:43.0965027Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T04:58:43.0965397Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0965746Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T04:58:43.0965991Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T04:58:43.0966361Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T04:58:43.0966714Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T04:58:43.0966956Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T04:58:43.0967184Z         
2025-03-14T04:58:43.0967630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T04:58:43.0968269Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T04:58:43.0968618Z         
2025-03-14T04:58:43.0969089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T04:58:43.0970350Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T04:58:43.0970798Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T04:58:43.0971097Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T04:58:43.0971406Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T04:58:43.0971724Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T04:58:43.0971987Z         
2025-03-14T04:58:43.0972559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:43.0973271Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T04:58:43.0973624Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:43.0973969Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T04:58:43.0974311Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:43.0974631Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:43.0974884Z         
2025-03-14T04:58:43.0975362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:43.0975908Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:43.0976155Z         
2025-03-14T04:58:45.8533798Z 
2025-03-14T04:58:45.8537787Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:45.8539573Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T04:58:45.8540032Z         l_scores_0_ = L_scores_0_
2025-03-14T04:58:45.8540280Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:58:45.8540509Z         
2025-03-14T04:58:45.8541188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:45.8541938Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:58:45.8542305Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:45.8542691Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:58:45.8543056Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:45.8543393Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:45.8543671Z         
2025-03-14T04:58:45.8544185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:45.8544902Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:45.8545180Z         
2025-03-14T04:58:45.8545293Z 
2025-03-14T04:58:45.8545396Z class GraphModule(torch.nn.Module):
2025-03-14T04:58:45.8545788Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T04:58:45.8546150Z         l_scores_0_ = L_scores_0_
2025-03-14T04:58:45.8546381Z         l_boxes_0_ = L_boxes_0_
2025-03-14T04:58:45.8546594Z         
2025-03-14T04:58:45.8547231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T04:58:45.8548352Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T04:58:45.8548716Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T04:58:45.8549094Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T04:58:45.8549455Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T04:58:45.8549781Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T04:58:45.8550045Z         
2025-03-14T04:58:45.8550550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T04:58:45.8551137Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T04:58:45.8551403Z         
2025-03-14T04:59:06.8983707Z Compilation time (from dynamo_timed): 85.925049743
2025-03-14T04:59:06.8984031Z pass
2025-03-14T04:59:06.8984409Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:59:06.8985465Z TIMING: entire_frame_compile:85.92505 gc:0.0548 _recursive_pre_grad_passes:0.04136 async_compile.wait:34.07125 backend_compile:65.06954 _recursive_joint_graph_passes:0.32713 _recursive_post_grad_passes:0.25971 code_gen:43.18766 inductor_compile:48.93965 total_wall_time:85.92505
2025-03-14T04:59:06.8986777Z STATS: call_* op count: 1160 | FakeTensorMode.__torch_dispatch__:42549 | FakeTensor.__torch_dispatch__:4401 | ProxyTorchDispatchMode.__torch_dispatch__:13994 | attempt fast:202 | slow no contiguity match:72 | fast is_contiguous:130
2025-03-14T04:59:06.8987491Z Dynamo produced 61 graphs covering 1160 ops with 46 graph breaks (6 unique)
2025-03-14T04:59:13.3725466Z 
2025-03-14T04:59:26.2852526Z loading model: 0it [00:00, ?it/s]
2025-03-14T04:59:26.2852919Z loading model: 0it [00:12, ?it/s]
2025-03-14T04:59:26.2867811Z cpu  eval  detectron2_fasterrcnn_r_50_c4      
2025-03-14T04:59:34.0749172Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_c4. Setting accuracy check to cosine
2025-03-14T04:59:34.0811748Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T04:59:53.7889661Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:00:15.0515752Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:00:24.7913870Z 
2025-03-14T05:00:24.7918459Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:24.7977011Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:00:24.8022534Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:24.8023163Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8024055Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8024989Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8025844Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8026665Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8027516Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8028365Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8029414Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8030350Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8031272Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8032131Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8033014Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8033903Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8034715Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8035494Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8036238Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8036969Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8037755Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8038531Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8039287Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8039998Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8040766Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8041590Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8042390Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8043171Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8043895Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8044643Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8045427Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8046211Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8046990Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8062252Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8063248Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8064327Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8065245Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8067343Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8068092Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8068870Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8069704Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8070492Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8071272Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8072011Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8072780Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8073578Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8074358Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8075094Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8075798Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8076557Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8077345Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8078099Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8078831Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8079523Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8080250Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8081054Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8082104Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8082868Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8083570Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8084297Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8085082Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8085842Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8086573Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8087267Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8087997Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8088779Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8089531Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8090263Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8091001Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8091740Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8092530Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8093298Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8094038Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8094764Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8095563Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8096398Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8097211Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8097987Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8098699Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8099425Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8100222Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8101025Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8101798Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8102531Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8103300Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8104192Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8105023Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8105824Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8106547Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8107269Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8108056Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8108815Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8109544Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8110265Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8110992Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8111787Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8112586Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8113319Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8114017Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8114749Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8115543Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8116302Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8117033Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8117737Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8118460Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8119241Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8119998Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8120753Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8121451Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8122175Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8122955Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8123713Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8124437Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8125147Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8125903Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8126699Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8127446Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8128159Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8128841Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8129559Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8130324Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8131072Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8131795Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8132498Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8133230Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8134018Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8134817Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8135567Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8136261Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8136969Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8137731Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8138480Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8139229Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8139919Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8140626Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8141406Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8142156Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8142889Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8143605Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8144426Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8145238Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8146034Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8146796Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8147502Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8148236Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8149011Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8149798Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8150521Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8151219Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8151953Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8152734Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8153501Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8154258Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8154985Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8155732Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8156507Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8157265Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8157994Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8158677Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8159394Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8160174Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8160913Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8161624Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8162304Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8163003Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8163811Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8164569Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8165308Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8165990Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8166707Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8167482Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8168281Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8169061Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8169789Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8170525Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8171316Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8172078Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8172816Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8173513Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8174242Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8175033Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8175796Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8176523Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8177220Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8177943Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8178743Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8179500Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8180265Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8181010Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8181911Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8182816Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8183634Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8184515Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8185309Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8186124Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8186931Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8187698Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8188474Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8189214Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8189985Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8190836Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8191634Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8192366Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8193100Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8193905Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8194703Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8195502Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8196278Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8197015Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8197773Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8198623Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8199450Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8200231Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8200966Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8201729Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8202557Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8203357Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8204123Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8204940Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:24.8205749Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:24.8206503Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:24.8207288Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:24.8208150Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:24.8209012Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:24.8209820Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:24.8210327Z         
2025-03-14T05:00:24.8210780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8211645Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8212312Z         
2025-03-14T05:00:24.8212705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8214630Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8216364Z         
2025-03-14T05:00:24.8216798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:24.8217334Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:24.8217633Z         
2025-03-14T05:00:24.8218137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:24.8218869Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:24.8219272Z         
2025-03-14T05:00:24.8219660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8220498Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8221121Z         
2025-03-14T05:00:24.8221537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8223684Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8225710Z         
2025-03-14T05:00:24.8226153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8226720Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:24.8227023Z         
2025-03-14T05:00:24.8227422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8228291Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8228930Z         
2025-03-14T05:00:24.8229343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8231529Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8233449Z         
2025-03-14T05:00:24.8233882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8234446Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:24.8234751Z         
2025-03-14T05:00:24.8235148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8236013Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8236661Z         
2025-03-14T05:00:24.8237054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8239123Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8240993Z         
2025-03-14T05:00:24.8241390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8242239Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8242867Z         
2025-03-14T05:00:24.8243267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8245467Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8247344Z         
2025-03-14T05:00:24.8247756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8248297Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:24.8248596Z         
2025-03-14T05:00:24.8249017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8249572Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:24.8249875Z         
2025-03-14T05:00:24.8250251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8251075Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8251678Z         
2025-03-14T05:00:24.8252076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8254126Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8255970Z         
2025-03-14T05:00:24.8256393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8256945Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:24.8257245Z         
2025-03-14T05:00:24.8257643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8258491Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8259133Z         
2025-03-14T05:00:24.8259540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8261696Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8263572Z         
2025-03-14T05:00:24.8264005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8264634Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:24.8264944Z         
2025-03-14T05:00:24.8265338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8266197Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8266837Z         
2025-03-14T05:00:24.8267242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8269396Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8271292Z         
2025-03-14T05:00:24.8271724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8272327Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:24.8272639Z         
2025-03-14T05:00:24.8273068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8273638Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:24.8273950Z         
2025-03-14T05:00:24.8274339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8275187Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8275814Z         
2025-03-14T05:00:24.8276223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8278416Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8280314Z         
2025-03-14T05:00:24.8280743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8281303Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:24.8281792Z         
2025-03-14T05:00:24.8282186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8283010Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8283632Z         
2025-03-14T05:00:24.8284029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8286122Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8288084Z         
2025-03-14T05:00:24.8288518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8289074Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:24.8289373Z         
2025-03-14T05:00:24.8289764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8290617Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8291260Z         
2025-03-14T05:00:24.8291672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8293873Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8295817Z         
2025-03-14T05:00:24.8296250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8296826Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:24.8297147Z         
2025-03-14T05:00:24.8297575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8298156Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:24.8298466Z         
2025-03-14T05:00:24.8298847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8299670Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8300280Z         
2025-03-14T05:00:24.8300673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8302754Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8304708Z         
2025-03-14T05:00:24.8305150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8305722Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:24.8306028Z         
2025-03-14T05:00:24.8306428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8307283Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8307904Z         
2025-03-14T05:00:24.8308299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8310442Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8312334Z         
2025-03-14T05:00:24.8312772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8313331Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:24.8313652Z         
2025-03-14T05:00:24.8314039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8314872Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8315494Z         
2025-03-14T05:00:24.8315889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8317968Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8319757Z         
2025-03-14T05:00:24.8320122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8320924Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8321524Z         
2025-03-14T05:00:24.8321898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8323995Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8325892Z         
2025-03-14T05:00:24.8326297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8326837Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:24.8327139Z         
2025-03-14T05:00:24.8327555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8328107Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:24.8328415Z         
2025-03-14T05:00:24.8328792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8329616Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8330187Z         
2025-03-14T05:00:24.8330558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8332622Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8334470Z         
2025-03-14T05:00:24.8334863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8335393Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:24.8335689Z         
2025-03-14T05:00:24.8336064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8336881Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8337497Z         
2025-03-14T05:00:24.8337890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8339981Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8341824Z         
2025-03-14T05:00:24.8342246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8342800Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:24.8343102Z         
2025-03-14T05:00:24.8343485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8344382Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8345036Z         
2025-03-14T05:00:24.8345461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8347554Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8349402Z         
2025-03-14T05:00:24.8349816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8350409Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:24.8350719Z         
2025-03-14T05:00:24.8351136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8351687Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:24.8351994Z         
2025-03-14T05:00:24.8352374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8353153Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8353725Z         
2025-03-14T05:00:24.8354104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8356091Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8357832Z         
2025-03-14T05:00:24.8358227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8358746Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:24.8359031Z         
2025-03-14T05:00:24.8359391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8360173Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8360757Z         
2025-03-14T05:00:24.8361132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8363081Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8364824Z         
2025-03-14T05:00:24.8365217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8365724Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:24.8366008Z         
2025-03-14T05:00:24.8366366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8367146Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8367725Z         
2025-03-14T05:00:24.8368100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8370082Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8371796Z         
2025-03-14T05:00:24.8372191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8372711Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:24.8373006Z         
2025-03-14T05:00:24.8373399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8373925Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:24.8374219Z         
2025-03-14T05:00:24.8374581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8375359Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8375934Z         
2025-03-14T05:00:24.8376310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8378259Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8380003Z         
2025-03-14T05:00:24.8380391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8380908Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:24.8381189Z         
2025-03-14T05:00:24.8381761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8382578Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8383204Z         
2025-03-14T05:00:24.8383595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8385813Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8387675Z         
2025-03-14T05:00:24.8388100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8388645Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:24.8388943Z         
2025-03-14T05:00:24.8389325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8390157Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8390770Z         
2025-03-14T05:00:24.8391166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8393265Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8395107Z         
2025-03-14T05:00:24.8395519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8396066Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:24.8396373Z         
2025-03-14T05:00:24.8396789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8397319Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:24.8397610Z         
2025-03-14T05:00:24.8397969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8398734Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8399292Z         
2025-03-14T05:00:24.8399680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8401721Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8403436Z         
2025-03-14T05:00:24.8403830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8404338Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:24.8404612Z         
2025-03-14T05:00:24.8404970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8405751Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8406329Z         
2025-03-14T05:00:24.8406723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8408767Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8410540Z         
2025-03-14T05:00:24.8410968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8411509Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:24.8411799Z         
2025-03-14T05:00:24.8412177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8412980Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8413590Z         
2025-03-14T05:00:24.8413982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8416127Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8417998Z         
2025-03-14T05:00:24.8418378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8419209Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8419833Z         
2025-03-14T05:00:24.8420225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8422339Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8424306Z         
2025-03-14T05:00:24.8424757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8425341Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:24.8425653Z         
2025-03-14T05:00:24.8426073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8426618Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:24.8426913Z         
2025-03-14T05:00:24.8427294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8428101Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8428699Z         
2025-03-14T05:00:24.8429096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8431201Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8433028Z         
2025-03-14T05:00:24.8433447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8433987Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:24.8434276Z         
2025-03-14T05:00:24.8434661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8435488Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8436098Z         
2025-03-14T05:00:24.8436497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8438558Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8440372Z         
2025-03-14T05:00:24.8440818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8441358Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:24.8441650Z         
2025-03-14T05:00:24.8442032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8442866Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8443455Z         
2025-03-14T05:00:24.8443856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8445981Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8447746Z         
2025-03-14T05:00:24.8448159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8448701Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:24.8448998Z         
2025-03-14T05:00:24.8449406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8449948Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:24.8450246Z         
2025-03-14T05:00:24.8450607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8451429Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8451996Z         
2025-03-14T05:00:24.8452392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8454401Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8456188Z         
2025-03-14T05:00:24.8456609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8457149Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:24.8457444Z         
2025-03-14T05:00:24.8457837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8458676Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8459309Z         
2025-03-14T05:00:24.8459704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8461823Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8463690Z         
2025-03-14T05:00:24.8464175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8464756Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:24.8465057Z         
2025-03-14T05:00:24.8465451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8466302Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8466944Z         
2025-03-14T05:00:24.8467342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8469493Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8471384Z         
2025-03-14T05:00:24.8471833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8472394Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:24.8472703Z         
2025-03-14T05:00:24.8473149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8473722Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:24.8474029Z         
2025-03-14T05:00:24.8474423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8475269Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8475889Z         
2025-03-14T05:00:24.8476303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8478477Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8480355Z         
2025-03-14T05:00:24.8480787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8481346Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:24.8481777Z         
2025-03-14T05:00:24.8482181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8483052Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8483688Z         
2025-03-14T05:00:24.8484099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8486215Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8488128Z         
2025-03-14T05:00:24.8488557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8489104Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:24.8489400Z         
2025-03-14T05:00:24.8489789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8490638Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8491264Z         
2025-03-14T05:00:24.8491675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8493819Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8495677Z         
2025-03-14T05:00:24.8496095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8496636Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:24.8496935Z         
2025-03-14T05:00:24.8497353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8497895Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:24.8498193Z         
2025-03-14T05:00:24.8498571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8499384Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8499988Z         
2025-03-14T05:00:24.8500384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8502466Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8504369Z         
2025-03-14T05:00:24.8504792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8505330Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:24.8505619Z         
2025-03-14T05:00:24.8506000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8506853Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8507481Z         
2025-03-14T05:00:24.8507885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8510147Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8512034Z         
2025-03-14T05:00:24.8512463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8513012Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:24.8513306Z         
2025-03-14T05:00:24.8513699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8514552Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8515178Z         
2025-03-14T05:00:24.8515579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8517692Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8519524Z         
2025-03-14T05:00:24.8519914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8520426Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:24.8520712Z         
2025-03-14T05:00:24.8521110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8521623Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:24.8521902Z         
2025-03-14T05:00:24.8522263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8523038Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8523608Z         
2025-03-14T05:00:24.8524000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8525959Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8527691Z         
2025-03-14T05:00:24.8528088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8528598Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:24.8528874Z         
2025-03-14T05:00:24.8529240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8530062Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8530676Z         
2025-03-14T05:00:24.8531076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8533150Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8534979Z         
2025-03-14T05:00:24.8535400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8535932Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:24.8536226Z         
2025-03-14T05:00:24.8536602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8537419Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8538028Z         
2025-03-14T05:00:24.8538421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8540505Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8542323Z         
2025-03-14T05:00:24.8542735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8543271Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:24.8543573Z         
2025-03-14T05:00:24.8543992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8544621Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:24.8544923Z         
2025-03-14T05:00:24.8545530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.8546269Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.8546585Z         
2025-03-14T05:00:24.8547012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.8547556Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:24.8547844Z         
2025-03-14T05:00:24.8548420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.8549125Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.8549457Z         
2025-03-14T05:00:24.8549873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.8550417Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:24.8550705Z         
2025-03-14T05:00:24.8551223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:24.8551899Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:24.8552275Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:24.8552585Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:24.8552855Z         
2025-03-14T05:00:24.8553325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:24.8553903Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:24.8554174Z         
2025-03-14T05:00:24.8554663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:24.8555230Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:24.8555501Z         
2025-03-14T05:00:24.8556055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:24.8556795Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:24.8557165Z         
2025-03-14T05:00:24.8557728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:24.8558388Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:24.8559065Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:24.8559724Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:24.8560048Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:24.8560315Z         
2025-03-14T05:00:24.8560749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.8561285Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:00:24.8561561Z         
2025-03-14T05:00:24.8561937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8563077Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:24.8563999Z         
2025-03-14T05:00:24.8564406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:24.8565012Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:00:24.8565340Z         
2025-03-14T05:00:24.8565867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:24.8567276Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:24.8568311Z         
2025-03-14T05:00:24.8568783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:24.8570101Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:24.8571092Z         
2025-03-14T05:00:24.8571539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:24.8572131Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:24.8572518Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:24.8572807Z         
2025-03-14T05:00:24.8573373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:24.8574067Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:00:24.8574500Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:00:24.8574946Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:00:24.8575278Z         
2025-03-14T05:00:24.8575820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:24.8576564Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:24.8576923Z         
2025-03-14T05:00:24.8577498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:24.8578217Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:24.8578604Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:24.8578982Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:24.8579306Z         
2025-03-14T05:00:24.8579829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:24.8580494Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:24.8580823Z         
2025-03-14T05:00:24.8581273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:24.8582064Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:24.8582381Z         
2025-03-14T05:00:24.8582855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:24.8583439Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:24.8583803Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.8584225Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:24.8584538Z         
2025-03-14T05:00:24.8585061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:24.8585624Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:24.8585996Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:24.8586386Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:24.8586692Z         
2025-03-14T05:00:24.8587146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:24.8587705Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.8588011Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:24.8588312Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:00:24.8588589Z         
2025-03-14T05:00:24.8589044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:24.8589623Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:24.8589958Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:24.8590274Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:00:24.8590557Z         
2025-03-14T05:00:24.8591015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:24.8591555Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:24.8591902Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:24.8592152Z         
2025-03-14T05:00:24.8592572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:24.8593117Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:24.8593464Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:24.8593718Z         
2025-03-14T05:00:24.8594135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:24.8594699Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:24.8595040Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:00:24.8595291Z         
2025-03-14T05:00:24.8595704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:24.8596267Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:24.8596639Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:00:24.8596890Z         
2025-03-14T05:00:24.8597333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:24.8597903Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:24.8598171Z         
2025-03-14T05:00:24.8598615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:24.8599173Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:24.8599469Z         
2025-03-14T05:00:24.8599953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:24.8600523Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:24.8600891Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:00:24.8601252Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:24.8601634Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:00:24.8601917Z         
2025-03-14T05:00:24.8602382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:24.8602955Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:24.8603296Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:00:24.8603649Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:24.8604023Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:00:24.8604304Z         
2025-03-14T05:00:24.8604753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:24.8605294Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:24.8605648Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:24.8606020Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:00:24.8606292Z         
2025-03-14T05:00:24.8606739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:24.8607272Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:24.8607631Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:24.8608011Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:00:24.8608313Z         
2025-03-14T05:00:24.8608755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:24.8609262Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:24.8609547Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:24.8609798Z         
2025-03-14T05:00:24.8610218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:24.8610710Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:24.8610991Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:24.8611240Z         
2025-03-14T05:00:24.8611651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:24.8612197Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:24.8612536Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:24.8612805Z         
2025-03-14T05:00:24.8613267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:24.8613819Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:24.8614158Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:24.8614450Z         
2025-03-14T05:00:24.8614954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:24.8615615Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:24.8615947Z         
2025-03-14T05:00:24.8616415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:24.8617022Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:24.8617338Z         
2025-03-14T05:00:24.8617858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:24.8618530Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:24.8618853Z         
2025-03-14T05:00:24.8619484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:24.8620235Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:24.8620519Z         
2025-03-14T05:00:24.8620955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.8621504Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:24.8621793Z         
2025-03-14T05:00:24.8622375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:24.8623039Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:00:24.8623347Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:24.8623678Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:24.8623940Z         
2025-03-14T05:00:24.8624644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:24.8625428Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:24.8625956Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:00:24.8626347Z         
2025-03-14T05:00:24.8626956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:24.8627725Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:24.8628059Z         
2025-03-14T05:00:24.8628466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.8629021Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:24.8629309Z         
2025-03-14T05:00:24.8629830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:24.8630463Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:24.8630749Z         
2025-03-14T05:00:24.8631158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.8631675Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:00:24.8631952Z         
2025-03-14T05:00:24.8632440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:24.8633038Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:24.8633317Z         
2025-03-14T05:00:24.8633921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:24.8634624Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:00:24.8634964Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:24.8635314Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:24.8635678Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:24.8635967Z         
2025-03-14T05:00:24.8636450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:24.8637021Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:24.8637273Z         
2025-03-14T05:00:24.8637716Z 
2025-03-14T05:00:24.8637832Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:24.8685110Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:00:24.8728364Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:24.8728892Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8729719Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8730632Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8731405Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8732146Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8732886Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8733624Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8734374Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8735109Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8735820Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8736497Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8737227Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8738064Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8738882Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8739666Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8740369Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8741086Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8741874Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8742632Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8743374Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8744073Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8744940Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8745852Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8746721Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8747558Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8748354Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8749155Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8750020Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8750845Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8751658Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8752423Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8753609Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8754482Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8755293Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8756100Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8756830Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8757602Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8758424Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8759218Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8759993Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8760725Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8761522Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8762336Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8763134Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8763902Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8764630Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8765400Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8766190Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8766960Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8767710Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8768406Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8769139Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8769926Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8770689Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8771426Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8772122Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8772855Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8773643Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8774403Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8775137Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8775844Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8776561Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8777332Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8778085Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8778796Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8779445Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8780142Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8780896Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8781779Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8782503Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8783205Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8783950Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8784784Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8785639Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8786460Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8787200Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8787985Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8788848Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8789648Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8790475Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8791209Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8791974Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8792806Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8793614Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8794393Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8795155Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8795969Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8796783Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8797622Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8798416Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8799159Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8799929Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8800760Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8801548Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8802310Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8803040Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8803797Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8804617Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8805323Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8806031Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8806684Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8807369Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8808104Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8808827Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8809555Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8810262Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8810994Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8811786Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8812550Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8813278Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8813994Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8814737Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8815528Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8816290Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8817026Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8817736Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8818474Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8819251Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8820021Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8820738Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8821439Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8822155Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8822956Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8823760Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8824612Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8825404Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8826225Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8827067Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8827879Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8828664Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8829408Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8830176Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8830969Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8831778Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8832554Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8833320Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.8834098Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8834936Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8835818Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.8836626Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.8837338Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8838132Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8838928Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8839747Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8840522Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8841281Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8842072Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8842890Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8843688Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8844470Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8845202Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8845962Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8846784Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8847618Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8848386Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8849116Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8849863Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8850675Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8851447Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8852181Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8852876Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8853603Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8854383Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8855204Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8856022Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8856781Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8857555Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8858387Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8859198Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8859974Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8860706Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8861480Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8862306Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8863110Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8863930Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8864756Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8865597Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8866419Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8867232Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8867600Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8867937Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8868332Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8868746Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8869117Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8869498Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8869847Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8870248Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8870637Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8871013Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8871375Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8871703Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8872103Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8872493Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8872869Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8873226Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8873561Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8873969Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8874368Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8874748Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8875111Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8875448Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:24.8875842Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8876255Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8876636Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.8877072Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.8877400Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:24.8877800Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8878200Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8878560Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.8878921Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.8879243Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:24.8879635Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.8880012Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.8880380Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.8880727Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.8881142Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:24.8881780Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:24.8882139Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:24.8882569Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:24.8882975Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:24.8883377Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:24.8883806Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:24.8883899Z         
2025-03-14T05:00:24.8884271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8884849Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8884930Z         
2025-03-14T05:00:24.8885255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8886856Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8886937Z         
2025-03-14T05:00:24.8887274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:24.8887431Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:24.8887515Z         
2025-03-14T05:00:24.8887923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:24.8888201Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:24.8888282Z         
2025-03-14T05:00:24.8888584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8889091Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8889165Z         
2025-03-14T05:00:24.8889485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8891153Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8891238Z         
2025-03-14T05:00:24.8891587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8891778Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:24.8891868Z         
2025-03-14T05:00:24.8892159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8892657Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8892731Z         
2025-03-14T05:00:24.8893033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8894686Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8894767Z         
2025-03-14T05:00:24.8895099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8895271Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:24.8895340Z         
2025-03-14T05:00:24.8895613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8896088Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8896171Z         
2025-03-14T05:00:24.8896462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8898102Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8898181Z         
2025-03-14T05:00:24.8898456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8898946Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8899040Z         
2025-03-14T05:00:24.8899319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8900999Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8901084Z         
2025-03-14T05:00:24.8901395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8901565Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:24.8901641Z         
2025-03-14T05:00:24.8901966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8902134Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:24.8902215Z         
2025-03-14T05:00:24.8902494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8902970Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8903063Z         
2025-03-14T05:00:24.8903367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8905102Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8905193Z         
2025-03-14T05:00:24.8905528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8905692Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:24.8905798Z         
2025-03-14T05:00:24.8906091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8906608Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8906700Z         
2025-03-14T05:00:24.8907014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8908706Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8908792Z         
2025-03-14T05:00:24.8909118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8909279Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:24.8909358Z         
2025-03-14T05:00:24.8909638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8910114Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8910184Z         
2025-03-14T05:00:24.8910471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8912088Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8912168Z         
2025-03-14T05:00:24.8912469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8912632Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:24.8912709Z         
2025-03-14T05:00:24.8913023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8913195Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:24.8913270Z         
2025-03-14T05:00:24.8913560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8914020Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8914102Z         
2025-03-14T05:00:24.8914385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8916025Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8916110Z         
2025-03-14T05:00:24.8916430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8916596Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:24.8916669Z         
2025-03-14T05:00:24.8916961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8917431Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8917543Z         
2025-03-14T05:00:24.8917828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8919495Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8919579Z         
2025-03-14T05:00:24.8919898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8920082Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:24.8920157Z         
2025-03-14T05:00:24.8920442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8920943Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8921046Z         
2025-03-14T05:00:24.8921344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8923064Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8923152Z         
2025-03-14T05:00:24.8923467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8923650Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:24.8923727Z         
2025-03-14T05:00:24.8924054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8924229Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:24.8924317Z         
2025-03-14T05:00:24.8924599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8925083Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8925172Z         
2025-03-14T05:00:24.8925479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8927168Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8927245Z         
2025-03-14T05:00:24.8927599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8927763Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:24.8927864Z         
2025-03-14T05:00:24.8928144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8928651Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8928725Z         
2025-03-14T05:00:24.8929027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8930706Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8930784Z         
2025-03-14T05:00:24.8931111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8931283Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:24.8931361Z         
2025-03-14T05:00:24.8931622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8932084Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8932170Z         
2025-03-14T05:00:24.8932467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8934143Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8934220Z         
2025-03-14T05:00:24.8934516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8935033Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8935118Z         
2025-03-14T05:00:24.8935439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8937162Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8937248Z         
2025-03-14T05:00:24.8937562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8937739Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:24.8937814Z         
2025-03-14T05:00:24.8938138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8938310Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:24.8938394Z         
2025-03-14T05:00:24.8938671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8939151Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8939234Z         
2025-03-14T05:00:24.8939546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8941222Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8941296Z         
2025-03-14T05:00:24.8941618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8941775Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:24.8941853Z         
2025-03-14T05:00:24.8942144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8942645Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8942751Z         
2025-03-14T05:00:24.8943047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8944815Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8944897Z         
2025-03-14T05:00:24.8945231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8945398Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:24.8945473Z         
2025-03-14T05:00:24.8945766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8946257Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8946342Z         
2025-03-14T05:00:24.8946647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8948433Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8948518Z         
2025-03-14T05:00:24.8948852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8949039Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:24.8949117Z         
2025-03-14T05:00:24.8949477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8949651Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:24.8949734Z         
2025-03-14T05:00:24.8950050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8950562Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8950640Z         
2025-03-14T05:00:24.8950953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8952756Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8952842Z         
2025-03-14T05:00:24.8953188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8953352Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:24.8953435Z         
2025-03-14T05:00:24.8953723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8954222Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8954310Z         
2025-03-14T05:00:24.8954607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8956216Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8956296Z         
2025-03-14T05:00:24.8956618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8956767Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:24.8956868Z         
2025-03-14T05:00:24.8957132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8957607Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8957695Z         
2025-03-14T05:00:24.8957983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8959606Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8959687Z         
2025-03-14T05:00:24.8959993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8960154Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:24.8960228Z         
2025-03-14T05:00:24.8960530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8960694Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:24.8960763Z         
2025-03-14T05:00:24.8961034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8961484Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8961584Z         
2025-03-14T05:00:24.8961860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8963478Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8963559Z         
2025-03-14T05:00:24.8963871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8964031Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:24.8964115Z         
2025-03-14T05:00:24.8964388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8964853Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8964932Z         
2025-03-14T05:00:24.8965206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8966830Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8966911Z         
2025-03-14T05:00:24.8967212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8967367Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:24.8967435Z         
2025-03-14T05:00:24.8967707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8968156Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8968252Z         
2025-03-14T05:00:24.8968527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8970167Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8970248Z         
2025-03-14T05:00:24.8970543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8970726Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:24.8970796Z         
2025-03-14T05:00:24.8971146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8971328Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:24.8971433Z         
2025-03-14T05:00:24.8971710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8972188Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8972260Z         
2025-03-14T05:00:24.8972562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8974240Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8974318Z         
2025-03-14T05:00:24.8974641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8974793Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:24.8974878Z         
2025-03-14T05:00:24.8975157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8975629Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8975721Z         
2025-03-14T05:00:24.8976024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8977690Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8977766Z         
2025-03-14T05:00:24.8978113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8978265Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:24.8978367Z         
2025-03-14T05:00:24.8978662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8979143Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8979217Z         
2025-03-14T05:00:24.8979520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8981194Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8981270Z         
2025-03-14T05:00:24.8981696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8982184Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.8982267Z         
2025-03-14T05:00:24.8982568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8984338Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8984481Z         
2025-03-14T05:00:24.8984805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8984976Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:24.8985051Z         
2025-03-14T05:00:24.8985387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8985572Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:24.8985652Z         
2025-03-14T05:00:24.8985957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8986453Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8986534Z         
2025-03-14T05:00:24.8986826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8988509Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8988584Z         
2025-03-14T05:00:24.8988912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8989061Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:24.8989143Z         
2025-03-14T05:00:24.8989420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8989892Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.8989974Z         
2025-03-14T05:00:24.8990268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8991940Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8992013Z         
2025-03-14T05:00:24.8992321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8992471Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:24.8992541Z         
2025-03-14T05:00:24.8992827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8993330Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.8993425Z         
2025-03-14T05:00:24.8993704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8995299Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8995379Z         
2025-03-14T05:00:24.8995676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.8995843Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:24.8995912Z         
2025-03-14T05:00:24.8996218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8996367Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:24.8996445Z         
2025-03-14T05:00:24.8996706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.8997150Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.8997238Z         
2025-03-14T05:00:24.8997523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.8999098Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.8999179Z         
2025-03-14T05:00:24.8999487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.8999654Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:24.8999736Z         
2025-03-14T05:00:24.9000016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9000481Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9000550Z         
2025-03-14T05:00:24.9000839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9002420Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9002501Z         
2025-03-14T05:00:24.9002819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9002967Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:24.9003048Z         
2025-03-14T05:00:24.9003338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9003788Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9003857Z         
2025-03-14T05:00:24.9004143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9005745Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9005825Z         
2025-03-14T05:00:24.9006127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9006280Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:24.9006355Z         
2025-03-14T05:00:24.9006667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9006847Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:24.9006931Z         
2025-03-14T05:00:24.9007207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9007636Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9007716Z         
2025-03-14T05:00:24.9007994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9009582Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9009661Z         
2025-03-14T05:00:24.9009961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9010108Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:24.9010178Z         
2025-03-14T05:00:24.9010454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9010889Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9010985Z         
2025-03-14T05:00:24.9011262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9012843Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9012922Z         
2025-03-14T05:00:24.9013220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9013383Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:24.9013452Z         
2025-03-14T05:00:24.9013740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9014202Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9014282Z         
2025-03-14T05:00:24.9014562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9016206Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9016288Z         
2025-03-14T05:00:24.9016603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9016775Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:24.9016848Z         
2025-03-14T05:00:24.9017168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9017328Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:24.9017409Z         
2025-03-14T05:00:24.9017686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9018154Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9018242Z         
2025-03-14T05:00:24.9018548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9020217Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9020293Z         
2025-03-14T05:00:24.9020629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9020793Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:24.9020891Z         
2025-03-14T05:00:24.9021171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9021645Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9021721Z         
2025-03-14T05:00:24.9022026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9023699Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9023775Z         
2025-03-14T05:00:24.9024109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9024329Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:24.9024418Z         
2025-03-14T05:00:24.9024712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9025205Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9025317Z         
2025-03-14T05:00:24.9025630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9027317Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9027396Z         
2025-03-14T05:00:24.9027719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9027897Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:24.9027980Z         
2025-03-14T05:00:24.9028313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9028497Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:24.9028570Z         
2025-03-14T05:00:24.9028856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9029310Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9029390Z         
2025-03-14T05:00:24.9029692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9031381Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9031467Z         
2025-03-14T05:00:24.9031784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9031941Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:24.9032015Z         
2025-03-14T05:00:24.9032297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9032761Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9032858Z         
2025-03-14T05:00:24.9033168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9034795Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9034875Z         
2025-03-14T05:00:24.9035193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9035355Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:24.9035438Z         
2025-03-14T05:00:24.9035712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9036166Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9036236Z         
2025-03-14T05:00:24.9036520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9038098Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9038180Z         
2025-03-14T05:00:24.9038476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9038636Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:24.9038706Z         
2025-03-14T05:00:24.9039014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9039172Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:24.9039242Z         
2025-03-14T05:00:24.9039712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.9039893Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.9039971Z         
2025-03-14T05:00:24.9040284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9040439Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:24.9040509Z         
2025-03-14T05:00:24.9040974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.9041134Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.9041210Z         
2025-03-14T05:00:24.9041517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9041670Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:24.9041754Z         
2025-03-14T05:00:24.9042173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:24.9042363Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:24.9042495Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:24.9042626Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:24.9042701Z         
2025-03-14T05:00:24.9043054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:24.9043193Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:24.9043262Z         
2025-03-14T05:00:24.9043619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:24.9043750Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:24.9043828Z         
2025-03-14T05:00:24.9044234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:24.9044469Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:24.9044541Z         
2025-03-14T05:00:24.9045003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:24.9045146Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:24.9045626Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:24.9045765Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:24.9045900Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:24.9045973Z         
2025-03-14T05:00:24.9046318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.9046486Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:00:24.9046559Z         
2025-03-14T05:00:24.9046844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9047706Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:24.9047790Z         
2025-03-14T05:00:24.9048090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:24.9048308Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:00:24.9048382Z         
2025-03-14T05:00:24.9048845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:24.9049814Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:24.9049928Z         
2025-03-14T05:00:24.9050341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:24.9051253Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:24.9051339Z         
2025-03-14T05:00:24.9051732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:24.9051912Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:24.9052070Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:24.9052153Z         
2025-03-14T05:00:24.9052626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:24.9052814Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:00:24.9053011Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:00:24.9053220Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:00:24.9053311Z         
2025-03-14T05:00:24.9053782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:24.9054008Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:24.9054092Z         
2025-03-14T05:00:24.9054579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:24.9054755Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:24.9054916Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:24.9055078Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:24.9055152Z         
2025-03-14T05:00:24.9055574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:24.9055785Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:24.9055860Z         
2025-03-14T05:00:24.9056256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:24.9056416Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:24.9056518Z         
2025-03-14T05:00:24.9056889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:24.9057047Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:24.9057193Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.9057366Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:24.9057441Z         
2025-03-14T05:00:24.9057828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:24.9057972Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:24.9058117Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:24.9058286Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:24.9058378Z         
2025-03-14T05:00:24.9058728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:24.9058872Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.9058980Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:24.9059125Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:00:24.9059199Z         
2025-03-14T05:00:24.9059557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:24.9059724Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:24.9059836Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:24.9059984Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:00:24.9060068Z         
2025-03-14T05:00:24.9060471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:24.9060669Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:24.9060797Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:24.9060878Z         
2025-03-14T05:00:24.9061215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:24.9061393Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:24.9061522Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:24.9061607Z         
2025-03-14T05:00:24.9061942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:24.9062124Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:24.9062249Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:00:24.9062330Z         
2025-03-14T05:00:24.9062687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:24.9062907Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:24.9063054Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:00:24.9063147Z         
2025-03-14T05:00:24.9063530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:24.9063690Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:24.9063771Z         
2025-03-14T05:00:24.9064202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:24.9064379Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:24.9064451Z         
2025-03-14T05:00:24.9064849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:24.9065008Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:24.9065163Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:00:24.9065331Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:24.9065495Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:00:24.9065565Z         
2025-03-14T05:00:24.9065957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:24.9066107Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:24.9066252Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:00:24.9066416Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:24.9066575Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:00:24.9066647Z         
2025-03-14T05:00:24.9067017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:24.9067168Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:24.9067352Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:24.9067495Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:00:24.9067575Z         
2025-03-14T05:00:24.9067934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:24.9068073Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:24.9068252Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:24.9068404Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:00:24.9068476Z         
2025-03-14T05:00:24.9068823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:24.9068930Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:24.9069084Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:24.9069158Z         
2025-03-14T05:00:24.9069535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:24.9069658Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:24.9069790Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:24.9069861Z         
2025-03-14T05:00:24.9070206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:24.9070330Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:24.9070470Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:24.9070559Z         
2025-03-14T05:00:24.9070885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:24.9071004Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:24.9071144Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:24.9071213Z         
2025-03-14T05:00:24.9071588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:24.9071781Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:24.9071858Z         
2025-03-14T05:00:24.9072207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:24.9072387Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:24.9072456Z         
2025-03-14T05:00:24.9072868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:24.9073060Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:24.9073131Z         
2025-03-14T05:00:24.9073641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:24.9073802Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:24.9073881Z         
2025-03-14T05:00:24.9074191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9074346Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:24.9074418Z         
2025-03-14T05:00:24.9074883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:24.9075003Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:00:24.9075122Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:24.9075243Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:24.9075319Z         
2025-03-14T05:00:24.9075816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:24.9075999Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:24.9076270Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:00:24.9076361Z         
2025-03-14T05:00:24.9076839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:24.9077022Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:24.9077091Z         
2025-03-14T05:00:24.9077409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9077567Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:24.9077645Z         
2025-03-14T05:00:24.9078045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:24.9078214Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:24.9078288Z         
2025-03-14T05:00:24.9078612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.9078769Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:00:24.9078847Z         
2025-03-14T05:00:24.9079248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:24.9079403Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:24.9079475Z         
2025-03-14T05:00:24.9079985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:24.9080151Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:00:24.9080294Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:24.9080464Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:24.9080602Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:24.9080681Z         
2025-03-14T05:00:24.9081061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:24.9081190Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:24.9081262Z         
2025-03-14T05:00:24.9081271Z 
2025-03-14T05:00:24.9081377Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:24.9126180Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:00:24.9126727Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:00:24.9127090Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9127526Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9127959Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9128381Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9128782Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9129175Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9129625Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9130086Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9130500Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9130924Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9131290Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9131736Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9132180Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9132577Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9132971Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9133322Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9133806Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9134227Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9134635Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9135039Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9135419Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.9135870Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9136316Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9136747Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.9137171Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.9137543Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9137969Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9138397Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9138810Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9139207Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9139572Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9139994Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9140414Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9140816Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9141219Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9141596Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9142022Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9142445Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9142842Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9143234Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9143593Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9144049Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9144540Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9144989Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9145405Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9145732Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9146128Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9146504Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9146873Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9147223Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9147557Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9147946Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9148337Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9148704Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9149055Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9149400Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9149788Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9150182Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9150536Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9150901Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9151220Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9151633Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9152044Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9152416Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9152780Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9153099Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9153498Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9153877Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9154259Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9154598Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9154947Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.9155329Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9155721Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9156096Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.9156470Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.9156790Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9157165Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9157541Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9157892Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9158245Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9158585Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9158974Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9159377Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9159728Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9160077Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9160392Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9160777Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9161147Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9161508Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9161860Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9162175Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9162560Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9162925Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9163285Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9163656Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9163983Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9164362Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9164744Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9165111Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9165457Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9165799Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9166202Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9166611Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9166976Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9167339Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9167661Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9168059Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9168447Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9168804Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9169165Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9169489Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9169881Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9170264Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9170650Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9170998Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9171326Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9171710Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9172086Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9172464Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9172881Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9173266Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9173714Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9174131Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9174488Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9174825Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9175138Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9175496Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9175860Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9176197Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9176529Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9176826Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9177193Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9177546Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9177910Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9178247Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9178579Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:00:24.9178980Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9179363Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9179736Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:00:24.9180082Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:00:24.9180426Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9180823Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9181210Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9181738Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9182094Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9182423Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9182803Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9183189Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9183549Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9183909Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9184270Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9184669Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9185120Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9185488Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9185856Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9186185Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9186593Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9186982Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9187384Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9187779Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9188156Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9188562Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9188946Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9189330Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9189687Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9190027Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9190430Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9190827Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9191203Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9191571Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9191908Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9192314Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9192709Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9193076Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9193454Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9193783Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9194183Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9194592Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9194986Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9195348Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9195692Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9196089Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9196489Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9196865Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9197226Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9197564Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9197958Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9198354Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9198731Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9199100Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9199425Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9199824Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9200207Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9200564Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9200921Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9201237Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9201624Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9202019Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9202392Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9202761Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9203080Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:00:24.9203466Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9203840Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9204203Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:00:24.9204549Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:00:24.9204871Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:00:24.9205256Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9205627Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9205991Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:00:24.9206338Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:00:24.9206679Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:00:24.9207060Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:00:24.9207444Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:00:24.9207800Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:00:24.9208159Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:00:24.9208555Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:24.9208929Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:24.9209299Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:24.9209728Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:24.9210131Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:24.9210508Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:24.9210903Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:24.9210985Z         
2025-03-14T05:00:24.9211319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9211851Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9211937Z         
2025-03-14T05:00:24.9212254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9213792Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9213894Z         
2025-03-14T05:00:24.9214200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:00:24.9214359Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:00:24.9214428Z         
2025-03-14T05:00:24.9214821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:00:24.9215083Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:00:24.9215164Z         
2025-03-14T05:00:24.9215455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9215922Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9215997Z         
2025-03-14T05:00:24.9216310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9217973Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9218065Z         
2025-03-14T05:00:24.9218394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9218557Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:00:24.9218629Z         
2025-03-14T05:00:24.9218916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9219385Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9219468Z         
2025-03-14T05:00:24.9219764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9221457Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9221610Z         
2025-03-14T05:00:24.9221938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9222106Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:00:24.9222179Z         
2025-03-14T05:00:24.9222473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9222964Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9223048Z         
2025-03-14T05:00:24.9223347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9225232Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9225341Z         
2025-03-14T05:00:24.9225627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9226143Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.9226221Z         
2025-03-14T05:00:24.9226537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9228385Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9228475Z         
2025-03-14T05:00:24.9228800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9229019Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:00:24.9229100Z         
2025-03-14T05:00:24.9229430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9229617Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:00:24.9229691Z         
2025-03-14T05:00:24.9229990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9230479Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9230566Z         
2025-03-14T05:00:24.9230866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9232642Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9232751Z         
2025-03-14T05:00:24.9233082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9233257Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:00:24.9233333Z         
2025-03-14T05:00:24.9233635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9234131Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9234218Z         
2025-03-14T05:00:24.9234524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9236274Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9236379Z         
2025-03-14T05:00:24.9236701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9236859Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:00:24.9236928Z         
2025-03-14T05:00:24.9237201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9237683Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9237766Z         
2025-03-14T05:00:24.9238058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9239785Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9239882Z         
2025-03-14T05:00:24.9240194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9240372Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:00:24.9240445Z         
2025-03-14T05:00:24.9240769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9240935Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:00:24.9241014Z         
2025-03-14T05:00:24.9241291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9241766Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9241837Z         
2025-03-14T05:00:24.9242121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9243712Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9243818Z         
2025-03-14T05:00:24.9244129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9244278Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:00:24.9244355Z         
2025-03-14T05:00:24.9244629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9245095Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9245171Z         
2025-03-14T05:00:24.9245463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9247109Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9247199Z         
2025-03-14T05:00:24.9247507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9247654Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:00:24.9247733Z         
2025-03-14T05:00:24.9248001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9248470Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9248545Z         
2025-03-14T05:00:24.9248836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9250471Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9250560Z         
2025-03-14T05:00:24.9250863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9251026Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:00:24.9251106Z         
2025-03-14T05:00:24.9251419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9251600Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:00:24.9251675Z         
2025-03-14T05:00:24.9251962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9252434Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9252519Z         
2025-03-14T05:00:24.9252828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9254527Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9254624Z         
2025-03-14T05:00:24.9254941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9255115Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:00:24.9255189Z         
2025-03-14T05:00:24.9255475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9255955Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9256040Z         
2025-03-14T05:00:24.9256333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9258036Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9258136Z         
2025-03-14T05:00:24.9258456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9258627Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:00:24.9258700Z         
2025-03-14T05:00:24.9258989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9259469Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9259552Z         
2025-03-14T05:00:24.9259846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9261590Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9261688Z         
2025-03-14T05:00:24.9261970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9262473Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.9262548Z         
2025-03-14T05:00:24.9262850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9264723Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9264804Z         
2025-03-14T05:00:24.9265130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9265321Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:00:24.9265405Z         
2025-03-14T05:00:24.9265724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9265907Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:00:24.9265982Z         
2025-03-14T05:00:24.9266274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9266744Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9266832Z         
2025-03-14T05:00:24.9267138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9268897Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9269013Z         
2025-03-14T05:00:24.9269337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9269506Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:00:24.9269580Z         
2025-03-14T05:00:24.9269869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9270355Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9270430Z         
2025-03-14T05:00:24.9270733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9272416Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9272515Z         
2025-03-14T05:00:24.9272833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9273002Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:00:24.9273073Z         
2025-03-14T05:00:24.9273361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9273848Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9273925Z         
2025-03-14T05:00:24.9274230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9275922Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9276036Z         
2025-03-14T05:00:24.9276333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9276517Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:00:24.9276595Z         
2025-03-14T05:00:24.9276897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9277065Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:00:24.9277138Z         
2025-03-14T05:00:24.9277410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9277861Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9277941Z         
2025-03-14T05:00:24.9278220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9279866Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9279961Z         
2025-03-14T05:00:24.9280265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9280426Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:00:24.9280496Z         
2025-03-14T05:00:24.9280770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9281222Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9281303Z         
2025-03-14T05:00:24.9281777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9283543Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9283651Z         
2025-03-14T05:00:24.9283975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9284136Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:00:24.9284206Z         
2025-03-14T05:00:24.9284481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9284935Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9285017Z         
2025-03-14T05:00:24.9285300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9286910Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9287017Z         
2025-03-14T05:00:24.9287315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9287488Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:00:24.9287559Z         
2025-03-14T05:00:24.9287863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9288023Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:00:24.9288099Z         
2025-03-14T05:00:24.9288359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9288802Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9288872Z         
2025-03-14T05:00:24.9289171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9290784Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9290874Z         
2025-03-14T05:00:24.9291179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9291331Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:00:24.9291407Z         
2025-03-14T05:00:24.9291669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9292124Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9292194Z         
2025-03-14T05:00:24.9292482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9294078Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9294167Z         
2025-03-14T05:00:24.9294477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9294626Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:00:24.9294701Z         
2025-03-14T05:00:24.9294963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9295425Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9295496Z         
2025-03-14T05:00:24.9295781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9297429Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9297516Z         
2025-03-14T05:00:24.9297820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9297987Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:00:24.9298064Z         
2025-03-14T05:00:24.9298359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9298525Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:00:24.9298596Z         
2025-03-14T05:00:24.9298865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9299303Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9299382Z         
2025-03-14T05:00:24.9299657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9301274Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9301369Z         
2025-03-14T05:00:24.9301668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9301819Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:00:24.9301890Z         
2025-03-14T05:00:24.9302157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9302604Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9302683Z         
2025-03-14T05:00:24.9302972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9304750Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9304866Z         
2025-03-14T05:00:24.9305192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9305363Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:00:24.9305440Z         
2025-03-14T05:00:24.9305736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9306210Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9306295Z         
2025-03-14T05:00:24.9306588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9308260Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9308365Z         
2025-03-14T05:00:24.9308650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9309142Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:00:24.9309217Z         
2025-03-14T05:00:24.9309520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9311275Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9311364Z         
2025-03-14T05:00:24.9311689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9311847Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:00:24.9311926Z         
2025-03-14T05:00:24.9312245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9312416Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:00:24.9312488Z         
2025-03-14T05:00:24.9312778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9313240Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9313323Z         
2025-03-14T05:00:24.9313627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9315294Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9315453Z         
2025-03-14T05:00:24.9315774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9315932Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:00:24.9316005Z         
2025-03-14T05:00:24.9316291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9316766Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9316840Z         
2025-03-14T05:00:24.9317143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9318849Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9318953Z         
2025-03-14T05:00:24.9319270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9319428Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:00:24.9319500Z         
2025-03-14T05:00:24.9319785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9320259Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9320332Z         
2025-03-14T05:00:24.9320635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9322247Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9322328Z         
2025-03-14T05:00:24.9322644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9322808Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:00:24.9322884Z         
2025-03-14T05:00:24.9323184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9323342Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:00:24.9323413Z         
2025-03-14T05:00:24.9323687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9324126Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9324204Z         
2025-03-14T05:00:24.9324479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9326117Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9326214Z         
2025-03-14T05:00:24.9326518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9326668Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:00:24.9326739Z         
2025-03-14T05:00:24.9327014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9327455Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9327535Z         
2025-03-14T05:00:24.9327814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9329415Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9329831Z         
2025-03-14T05:00:24.9330132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9330280Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:00:24.9330351Z         
2025-03-14T05:00:24.9330621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9331064Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9331142Z         
2025-03-14T05:00:24.9331422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9333047Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9333188Z         
2025-03-14T05:00:24.9333481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9333642Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:00:24.9333713Z         
2025-03-14T05:00:24.9334013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9334165Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:00:24.9334243Z         
2025-03-14T05:00:24.9334503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9334939Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9335008Z         
2025-03-14T05:00:24.9335295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9336889Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9336985Z         
2025-03-14T05:00:24.9337295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9337438Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:00:24.9337516Z         
2025-03-14T05:00:24.9337778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9338224Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9338294Z         
2025-03-14T05:00:24.9338577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9340186Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9340282Z         
2025-03-14T05:00:24.9340591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9340732Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:00:24.9340811Z         
2025-03-14T05:00:24.9341072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9341517Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9341588Z         
2025-03-14T05:00:24.9341872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9343479Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9343570Z         
2025-03-14T05:00:24.9343880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9344039Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:00:24.9344176Z         
2025-03-14T05:00:24.9344491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9344653Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:00:24.9344721Z         
2025-03-14T05:00:24.9345017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9345496Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9345582Z         
2025-03-14T05:00:24.9345904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9347567Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9347670Z         
2025-03-14T05:00:24.9347997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9348155Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:00:24.9348230Z         
2025-03-14T05:00:24.9348524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9349010Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9349111Z         
2025-03-14T05:00:24.9349397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9351014Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9351116Z         
2025-03-14T05:00:24.9351431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9351587Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:00:24.9351659Z         
2025-03-14T05:00:24.9351942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9352409Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9352491Z         
2025-03-14T05:00:24.9352782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9354485Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9354588Z         
2025-03-14T05:00:24.9354901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9355068Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:00:24.9355136Z         
2025-03-14T05:00:24.9355448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9355598Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:00:24.9355677Z         
2025-03-14T05:00:24.9355940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9356380Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:00:24.9356451Z         
2025-03-14T05:00:24.9356740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9358354Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9358440Z         
2025-03-14T05:00:24.9358749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9358891Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:00:24.9358971Z         
2025-03-14T05:00:24.9359233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9359701Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:00:24.9359771Z         
2025-03-14T05:00:24.9360075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9361691Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9361781Z         
2025-03-14T05:00:24.9362091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9362231Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:00:24.9362310Z         
2025-03-14T05:00:24.9362570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9363021Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:00:24.9363100Z         
2025-03-14T05:00:24.9363383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:00:24.9365021Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:00:24.9365109Z         
2025-03-14T05:00:24.9365417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:00:24.9365569Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:00:24.9365646Z         
2025-03-14T05:00:24.9365947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:00:24.9366104Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:00:24.9366176Z         
2025-03-14T05:00:24.9366643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.9366808Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.9366891Z         
2025-03-14T05:00:24.9367215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9367376Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:24.9367469Z         
2025-03-14T05:00:24.9367920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:24.9368085Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:00:24.9368153Z         
2025-03-14T05:00:24.9368465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9368609Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:24.9368685Z         
2025-03-14T05:00:24.9369075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:24.9369273Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:24.9369378Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:24.9369514Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:24.9369585Z         
2025-03-14T05:00:24.9369938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:24.9370072Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:24.9370150Z         
2025-03-14T05:00:24.9370487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:24.9370618Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:24.9370688Z         
2025-03-14T05:00:24.9371092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:24.9371314Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:24.9371411Z         
2025-03-14T05:00:24.9371851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:24.9371991Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:24.9372441Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:24.9372583Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:24.9372702Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:24.9372782Z         
2025-03-14T05:00:24.9373094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.9373236Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:00:24.9373306Z         
2025-03-14T05:00:24.9373596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:24.9374417Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:24.9374515Z         
2025-03-14T05:00:24.9374814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:24.9375007Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:00:24.9375083Z         
2025-03-14T05:00:24.9375488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:24.9376392Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:24.9376466Z         
2025-03-14T05:00:24.9376853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:24.9377712Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:24.9377783Z         
2025-03-14T05:00:24.9378145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:24.9378325Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:24.9378480Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:24.9378549Z         
2025-03-14T05:00:24.9378993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:24.9379158Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:00:24.9379351Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:00:24.9379536Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:00:24.9379616Z         
2025-03-14T05:00:24.9380041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:24.9380273Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:24.9380342Z         
2025-03-14T05:00:24.9380804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:24.9380974Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:24.9381133Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:24.9381282Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:24.9381358Z         
2025-03-14T05:00:24.9381941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:24.9382136Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:24.9382217Z         
2025-03-14T05:00:24.9382546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:24.9382705Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:24.9382773Z         
2025-03-14T05:00:24.9383108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:24.9383249Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:24.9383391Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.9383543Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:24.9383622Z         
2025-03-14T05:00:24.9383958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:24.9384096Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:24.9384280Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:24.9384454Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:24.9384526Z         
2025-03-14T05:00:24.9384875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:24.9385060Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:24.9385169Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:24.9385308Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:00:24.9385388Z         
2025-03-14T05:00:24.9385731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:24.9385892Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:24.9385990Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:24.9386131Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:00:24.9386202Z         
2025-03-14T05:00:24.9386564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:24.9386727Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:24.9386878Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:24.9386950Z         
2025-03-14T05:00:24.9387277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:24.9387457Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:24.9387614Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:24.9387684Z         
2025-03-14T05:00:24.9388019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:24.9388186Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:24.9388313Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:00:24.9388385Z         
2025-03-14T05:00:24.9388721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:24.9388917Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:24.9389046Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:00:24.9389117Z         
2025-03-14T05:00:24.9389486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:24.9389645Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:24.9389715Z         
2025-03-14T05:00:24.9390083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:24.9390228Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:24.9390305Z         
2025-03-14T05:00:24.9390677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:24.9390834Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:24.9390967Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:00:24.9391135Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:24.9391300Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:00:24.9391375Z         
2025-03-14T05:00:24.9391740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:24.9391892Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:24.9392023Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:00:24.9392189Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:24.9392331Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:00:24.9392406Z         
2025-03-14T05:00:24.9392750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:24.9392878Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:24.9393044Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:24.9393208Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:00:24.9393277Z         
2025-03-14T05:00:24.9393650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:24.9393788Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:24.9393969Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:24.9394111Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:00:24.9394187Z         
2025-03-14T05:00:24.9394515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:24.9394626Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:24.9394752Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:24.9394833Z         
2025-03-14T05:00:24.9395159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:24.9395267Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:24.9395389Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:24.9395469Z         
2025-03-14T05:00:24.9395790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:24.9395923Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:24.9396056Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:24.9396134Z         
2025-03-14T05:00:24.9396452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:24.9396580Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:24.9396710Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:24.9396789Z         
2025-03-14T05:00:24.9397154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:24.9397368Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:24.9397438Z         
2025-03-14T05:00:24.9397793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:24.9397959Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:24.9398036Z         
2025-03-14T05:00:24.9398482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:24.9398672Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:24.9398739Z         
2025-03-14T05:00:24.9399252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:24.9399404Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:24.9399472Z         
2025-03-14T05:00:24.9399803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9399974Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:24.9400071Z         
2025-03-14T05:00:24.9400521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:24.9400652Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:00:24.9400764Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:24.9400891Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:24.9400958Z         
2025-03-14T05:00:24.9401444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:24.9401617Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:24.9401870Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:00:24.9401941Z         
2025-03-14T05:00:24.9402422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:24.9402598Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:24.9402674Z         
2025-03-14T05:00:24.9402981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:24.9403147Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:24.9403217Z         
2025-03-14T05:00:24.9403622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:24.9403780Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:24.9403860Z         
2025-03-14T05:00:24.9404188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:24.9404350Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:00:24.9404420Z         
2025-03-14T05:00:24.9404821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:24.9404970Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:24.9405049Z         
2025-03-14T05:00:24.9405547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:24.9405700Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:00:24.9405836Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:24.9406000Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:24.9406164Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:24.9406234Z         
2025-03-14T05:00:24.9406647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:24.9406788Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:24.9406866Z         
2025-03-14T05:00:33.4480416Z 
2025-03-14T05:00:33.4484542Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:33.4486203Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:00:33.4487805Z         l_features_res4_ = L_features_res4_
2025-03-14T05:00:33.4488221Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:33.4488762Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:33.4489256Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:33.4489794Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:33.4490389Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:33.4490964Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:33.4491510Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:33.4491877Z         
2025-03-14T05:00:33.4492511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:33.4493499Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:00:33.4493774Z         
2025-03-14T05:00:33.4494172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4494670Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:33.4494928Z         
2025-03-14T05:00:33.4495457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:33.4496097Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:00:33.4496368Z         
2025-03-14T05:00:33.4496761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4497265Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:33.4497533Z         
2025-03-14T05:00:33.4498109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:33.4498772Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:33.4499178Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:33.4499462Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:33.4499706Z         
2025-03-14T05:00:33.4500129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:33.4500661Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:33.4500916Z         
2025-03-14T05:00:33.4501343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:33.4501855Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:33.4502101Z         
2025-03-14T05:00:33.4502580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:33.4503237Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:33.4503571Z         
2025-03-14T05:00:33.4504205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:33.4504837Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:33.4505387Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:33.4505939Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:33.4506268Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:33.4506528Z         
2025-03-14T05:00:33.4506931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:33.4507440Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:00:33.4507690Z         
2025-03-14T05:00:33.4508046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:33.4508990Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:33.4509719Z         
2025-03-14T05:00:33.4510095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:33.4510625Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:33.4510933Z         
2025-03-14T05:00:33.4511411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:33.4512536Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:33.4513314Z         
2025-03-14T05:00:33.4513773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:33.4514803Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:33.4515524Z         
2025-03-14T05:00:33.4515967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:33.4516531Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:33.4516889Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:33.4517156Z         
2025-03-14T05:00:33.4517675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:33.4518311Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:00:33.4518700Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:00:33.4519112Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:00:33.4519415Z         
2025-03-14T05:00:33.4519915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:33.4520588Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:33.4520917Z         
2025-03-14T05:00:33.4522941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:33.4523606Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:33.4523965Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:33.4524311Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:33.4524573Z         
2025-03-14T05:00:33.4525041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:33.4525645Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:33.4525943Z         
2025-03-14T05:00:33.4526356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:33.4526923Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:33.4527310Z         
2025-03-14T05:00:33.4527868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:33.4528642Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:33.4529116Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:33.4530254Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:33.4530701Z         
2025-03-14T05:00:33.4531155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:33.4531701Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:33.4532025Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:33.4532355Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:33.4533374Z         
2025-03-14T05:00:33.4533819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:33.4534344Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:33.4534636Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:33.4534914Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:00:33.4535169Z         
2025-03-14T05:00:33.4535604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:33.4536128Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:33.4536436Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:33.4536714Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:00:33.4536957Z         
2025-03-14T05:00:33.4537383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:33.4537894Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:33.4538221Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:33.4538457Z         
2025-03-14T05:00:33.4538844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:33.4539384Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:33.4539705Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:33.4539937Z         
2025-03-14T05:00:33.4540325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:33.4540831Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:33.4541150Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:00:33.4541392Z         
2025-03-14T05:00:33.4541792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:33.4542343Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:33.4542701Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:00:33.4542944Z         
2025-03-14T05:00:33.4543408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:33.4543981Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:33.4544332Z         
2025-03-14T05:00:33.4544794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:33.4545348Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:33.4545622Z         
2025-03-14T05:00:33.4546079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:33.4546651Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:33.4546997Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:00:33.4547359Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:33.4547717Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:00:33.4547984Z         
2025-03-14T05:00:33.4548431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:33.4548979Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:33.4549309Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:00:33.4549643Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:33.4549995Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:00:33.4550257Z         
2025-03-14T05:00:33.4550674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:33.4551188Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:33.4551526Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:33.4551882Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:00:33.4552182Z         
2025-03-14T05:00:33.4552620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:33.4553147Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:33.4553496Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:33.4553860Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:00:33.4554127Z         
2025-03-14T05:00:33.4554546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:33.4555040Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:33.4555313Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:33.4555558Z         
2025-03-14T05:00:33.4555966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:33.4556448Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:33.4556736Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:33.4556982Z         
2025-03-14T05:00:33.4557398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:33.4557891Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:33.4558212Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:33.4558470Z         
2025-03-14T05:00:33.4558869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:33.4559395Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:33.4559701Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:33.4559949Z         
2025-03-14T05:00:33.4560379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:33.4560962Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:33.4561261Z         
2025-03-14T05:00:33.4561688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:33.4562240Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:33.4562527Z         
2025-03-14T05:00:33.4562989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:33.4563602Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:33.4563887Z         
2025-03-14T05:00:33.4564457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:33.4565161Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:33.4565409Z         
2025-03-14T05:00:33.4565795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4566308Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:33.4566574Z         
2025-03-14T05:00:33.4567100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:33.4567701Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:00:33.4567977Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:33.4568252Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:33.4568489Z         
2025-03-14T05:00:33.4569037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:33.4569714Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:33.4570169Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:00:33.4570521Z         
2025-03-14T05:00:33.4571084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:33.4571771Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:33.4572076Z         
2025-03-14T05:00:33.4572471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4572983Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:33.4573261Z         
2025-03-14T05:00:33.4573731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:33.4574314Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:33.4574580Z         
2025-03-14T05:00:33.4574975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:33.4575489Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:00:33.4575760Z         
2025-03-14T05:00:33.4576240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:33.4576812Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:33.4577072Z         
2025-03-14T05:00:33.4577657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:33.4578346Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:00:33.4578674Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:33.4579013Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:33.4579365Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:33.4579627Z         
2025-03-14T05:00:33.4580096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:33.4580673Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:33.4580920Z         
2025-03-14T05:00:33.4581104Z 
2025-03-14T05:00:33.4581203Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:33.4582968Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:00:33.4584404Z         l_features_res4_ = L_features_res4_
2025-03-14T05:00:33.4584846Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:00:33.4585538Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:00:33.4586089Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:00:33.4586679Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:00:33.4587290Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:00:33.4587874Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:00:33.4588446Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:00:33.4588824Z         
2025-03-14T05:00:33.4589389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:33.4590056Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:00:33.4590336Z         
2025-03-14T05:00:33.4590738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4591245Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:33.4591515Z         
2025-03-14T05:00:33.4592062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:00:33.4592728Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:00:33.4593011Z         
2025-03-14T05:00:33.4593413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4593922Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:00:33.4594192Z         
2025-03-14T05:00:33.4594679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:00:33.4595367Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:00:33.4595725Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:00:33.4596011Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:00:33.4596274Z         
2025-03-14T05:00:33.4596730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:00:33.4597330Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:00:33.4597588Z         
2025-03-14T05:00:33.4598009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:00:33.4598521Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:00:33.4598770Z         
2025-03-14T05:00:33.4599250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:00:33.4599926Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:00:33.4600256Z         
2025-03-14T05:00:33.4600780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:00:33.4601405Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:00:33.4601905Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:00:33.4602401Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:00:33.4602694Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:00:33.4602928Z         
2025-03-14T05:00:33.4603319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:33.4603796Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:00:33.4604041Z         
2025-03-14T05:00:33.4604390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:00:33.4605297Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:00:33.4606002Z         
2025-03-14T05:00:33.4606370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:00:33.4606881Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:00:33.4607184Z         
2025-03-14T05:00:33.4607652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:00:33.4608717Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:00:33.4609476Z         
2025-03-14T05:00:33.4609921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:00:33.4610916Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:00:33.4611625Z         
2025-03-14T05:00:33.4612044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:00:33.4612587Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:00:33.4612930Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:00:33.4613197Z         
2025-03-14T05:00:33.4613717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:00:33.4614351Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:00:33.4614746Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:00:33.4615140Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:00:33.4615438Z         
2025-03-14T05:00:33.4615925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:33.4616582Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:00:33.4616902Z         
2025-03-14T05:00:33.4617420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:33.4618057Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:00:33.4618420Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:33.4618783Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:33.4619057Z         
2025-03-14T05:00:33.4619550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:33.4620193Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:00:33.4620513Z         
2025-03-14T05:00:33.4620962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:33.4621531Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:33.4621807Z         
2025-03-14T05:00:33.4622235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:33.4622790Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:33.4623118Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:33.4623469Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:33.4623765Z         
2025-03-14T05:00:33.4624298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:33.4624866Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:33.4625204Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:33.4625563Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:33.4625847Z         
2025-03-14T05:00:33.4626273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:33.4626787Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:33.4627072Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:33.4627378Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:00:33.4627640Z         
2025-03-14T05:00:33.4628087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:33.4628627Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:33.4628924Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:33.4629204Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:00:33.4629458Z         
2025-03-14T05:00:33.4629878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:33.4630396Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:33.4630731Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:33.4630976Z         
2025-03-14T05:00:33.4631377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:33.4631893Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:33.4632228Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:33.4632470Z         
2025-03-14T05:00:33.4632867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:33.4633392Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:33.4633724Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:00:33.4633960Z         
2025-03-14T05:00:33.4634374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:33.4634913Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:33.4635262Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:00:33.4635500Z         
2025-03-14T05:00:33.4635928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:33.4636489Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:33.4636773Z         
2025-03-14T05:00:33.4637194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:33.4637720Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:33.4637977Z         
2025-03-14T05:00:33.4638411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:33.4638947Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:33.4639266Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:00:33.4639602Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:33.4639948Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:00:33.4640206Z         
2025-03-14T05:00:33.4640668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:33.4641211Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:33.4641551Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:00:33.4641907Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:33.4642248Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:00:33.4642508Z         
2025-03-14T05:00:33.4642933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:33.4643439Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:33.4643768Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:33.4644114Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:00:33.4644368Z         
2025-03-14T05:00:33.4644791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:33.4645297Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:33.4656531Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:33.4656962Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:00:33.4657248Z         
2025-03-14T05:00:33.4657724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:33.4658235Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:33.4658521Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:33.4658774Z         
2025-03-14T05:00:33.4659203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:33.4659686Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:33.4659963Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:33.4660213Z         
2025-03-14T05:00:33.4660629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:33.4661243Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:33.4661555Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:33.4661824Z         
2025-03-14T05:00:33.4662226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:33.4662733Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:33.4663038Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:33.4663303Z         
2025-03-14T05:00:33.4663756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:33.4664456Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:33.4664785Z         
2025-03-14T05:00:33.4665292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:33.4665869Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:33.4666165Z         
2025-03-14T05:00:33.4666826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:33.4667495Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:33.4667798Z         
2025-03-14T05:00:33.4668391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:33.4669109Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:33.4669389Z         
2025-03-14T05:00:33.4669788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4670303Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:00:33.4670578Z         
2025-03-14T05:00:33.4671114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:33.4671741Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:00:33.4672015Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:33.4672286Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:33.4672521Z         
2025-03-14T05:00:33.4673077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:33.4673772Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:33.4674243Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:00:33.4674611Z         
2025-03-14T05:00:33.4675153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:33.4675858Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:33.4676153Z         
2025-03-14T05:00:33.4676548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:33.4677069Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:33.4677350Z         
2025-03-14T05:00:33.4677827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:33.4678422Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:33.4678696Z         
2025-03-14T05:00:33.4679105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:33.4679600Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:00:33.4679873Z         
2025-03-14T05:00:33.4680365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:33.4680971Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:33.4681261Z         
2025-03-14T05:00:33.4682138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:33.4682829Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:00:33.4683151Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:33.4683492Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:33.4683851Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:33.4684116Z         
2025-03-14T05:00:33.4684589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:33.4685132Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:33.4685377Z         
2025-03-14T05:00:34.1157421Z 
2025-03-14T05:00:34.1160934Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:34.1163894Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T05:00:34.1167277Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:00:34.1168448Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:00:34.1169489Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:00:34.1170483Z         
2025-03-14T05:00:34.1171793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:00:34.1173237Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:00:34.1174278Z         
2025-03-14T05:00:34.1175731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:00:34.1177616Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:00:34.1178831Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:00:34.1179984Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:00:34.1180957Z         
2025-03-14T05:00:34.1183790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:34.1186882Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:00:34.1188105Z         
2025-03-14T05:00:34.1189371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:34.1190713Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:00:34.1191734Z         
2025-03-14T05:00:34.1192951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:34.1194757Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:34.1198506Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:34.1199995Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:00:34.1201153Z         
2025-03-14T05:00:34.1202418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:34.1203681Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:34.1213306Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:34.1215893Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:00:34.1217128Z         
2025-03-14T05:00:34.1218417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:34.1221947Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:34.1224976Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:00:34.1226296Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:00:34.1227336Z         
2025-03-14T05:00:34.1228612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:34.1229968Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:34.1231024Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:00:34.1234400Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:00:34.1236590Z         
2025-03-14T05:00:34.1238034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:34.1239413Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:34.1240586Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:00:34.1241598Z         
2025-03-14T05:00:34.1242771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:34.1245410Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:34.1245874Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:00:34.1246286Z         
2025-03-14T05:00:34.1247632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:34.1248190Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:34.1248533Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:00:34.1248824Z         
2025-03-14T05:00:34.1249244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:34.1249791Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:34.1250146Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:00:34.1250389Z         
2025-03-14T05:00:34.1250828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:34.1251481Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:34.1251745Z         
2025-03-14T05:00:34.1252221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:34.1252790Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:34.1253052Z         
2025-03-14T05:00:34.1253493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:34.1254039Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:34.1254364Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:00:34.1254706Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:34.1255062Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:00:34.1255324Z         
2025-03-14T05:00:34.1255761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:34.1256312Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:34.1256641Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:00:34.1256982Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:34.1257336Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:00:34.1257610Z         
2025-03-14T05:00:34.1258033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:34.1258539Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:34.1258879Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:34.1259236Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:00:34.1259498Z         
2025-03-14T05:00:34.1259932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:34.1260488Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:34.1260834Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:34.1261197Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:00:34.1261463Z         
2025-03-14T05:00:34.1261886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:34.1262374Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:34.1262673Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:34.1262921Z         
2025-03-14T05:00:34.1263344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:34.1263838Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:34.1264218Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:34.1264486Z         
2025-03-14T05:00:34.1264928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:34.1265432Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:34.1265767Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:34.1266049Z         
2025-03-14T05:00:34.1266448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:34.1266930Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:34.1267229Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:34.1267484Z         
2025-03-14T05:00:34.1267929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:34.1268567Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:34.1268871Z         
2025-03-14T05:00:34.1269308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:34.1269868Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:00:34.1270150Z         
2025-03-14T05:00:34.1270635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:00:34.1271255Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:00:34.1271552Z         
2025-03-14T05:00:34.1272137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:00:34.1272975Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:00:34.1273235Z         
2025-03-14T05:00:34.1273628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:34.1274130Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:00:34.1274417Z         
2025-03-14T05:00:34.1274954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:00:34.1275631Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:00:34.1275981Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:00:34.1276267Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:00:34.1276504Z         
2025-03-14T05:00:34.1277051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:00:34.1277727Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:00:34.1278185Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T05:00:34.1278538Z         
2025-03-14T05:00:34.1279103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:00:34.1279798Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:00:34.1280087Z         
2025-03-14T05:00:34.1280489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:00:34.1280993Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:00:34.1281270Z         
2025-03-14T05:00:34.1281982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:00:34.1282572Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:00:34.1282842Z         
2025-03-14T05:00:34.1283232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:00:34.1283731Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T05:00:34.1283997Z         
2025-03-14T05:00:34.1284459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:00:34.1285024Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:00:34.1285284Z         
2025-03-14T05:00:34.1285845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:00:34.1286513Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:00:34.1286822Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:34.1287155Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:00:34.1287495Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:00:34.1287749Z         
2025-03-14T05:00:34.1288200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:00:34.1288818Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:00:34.1289053Z         
2025-03-14T05:00:49.8922363Z 
2025-03-14T05:00:49.8922979Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:49.8924766Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:49.8933342Z         l_stack0_ = L_stack0_
2025-03-14T05:00:49.8933998Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:49.8934929Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:49.8935527Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:49.8936167Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:49.8936738Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:49.8937160Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:49.8937572Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:49.8937980Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:49.8938291Z         
2025-03-14T05:00:49.8938883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:00:49.8939564Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:00:49.8939838Z         
2025-03-14T05:00:49.8940256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:49.8941253Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:49.8942004Z         
2025-03-14T05:00:49.8942436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:49.8943477Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:49.8944475Z         
2025-03-14T05:00:49.8944913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.8945515Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.8945786Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:49.8946037Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:49.8946332Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.8946622Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:49.8946890Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:49.8947122Z         
2025-03-14T05:00:49.8947513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:49.8948476Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.8949216Z         
2025-03-14T05:00:49.8949681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:49.8950357Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:49.8950638Z         
2025-03-14T05:00:49.8951068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:49.8951633Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:49.8951928Z         
2025-03-14T05:00:49.8952340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:49.8952843Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:49.8953150Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.8953472Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:49.8953740Z         
2025-03-14T05:00:49.8954149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:49.8954648Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:49.8954946Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:49.8955261Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:49.8955525Z         
2025-03-14T05:00:49.8955925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:49.8956412Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.8956674Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:49.8956936Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:49.8957177Z         
2025-03-14T05:00:49.8957582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:49.8958095Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:49.8958384Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:49.8958650Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:49.8958919Z         
2025-03-14T05:00:49.8959335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:49.8959851Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:49.8960186Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:49.8960428Z         
2025-03-14T05:00:49.8960832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:49.8961355Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:49.8961689Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:49.8961939Z         
2025-03-14T05:00:49.8962328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:49.8962826Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:49.8963144Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:49.8963379Z         
2025-03-14T05:00:49.8963792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:49.8964346Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:49.8964711Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:49.8964949Z         
2025-03-14T05:00:49.8965395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:49.8965950Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:49.8966223Z         
2025-03-14T05:00:49.8966638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:49.8967192Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:49.8967462Z         
2025-03-14T05:00:49.8967906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:49.8968470Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:49.8968805Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:49.8969159Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:49.8969523Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:49.8969799Z         
2025-03-14T05:00:49.8970263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:49.8970833Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:49.8971169Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:49.8971517Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:49.8971884Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:49.8972161Z         
2025-03-14T05:00:49.8972626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:49.8973156Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:49.8973502Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:49.8973866Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:49.8974131Z         
2025-03-14T05:00:49.8974607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:49.8975140Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:49.8975488Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:49.8975854Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:49.8976121Z         
2025-03-14T05:00:49.8976542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:49.8977044Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:49.8977324Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:49.8977574Z         
2025-03-14T05:00:49.8978018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:49.8978527Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:49.8978802Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:49.8979048Z         
2025-03-14T05:00:49.8979463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:49.8979966Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:49.8980272Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:49.8980536Z         
2025-03-14T05:00:49.8980948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:49.8981686Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:49.8982007Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:49.8982271Z         
2025-03-14T05:00:49.8982731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:49.8983350Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:49.8983661Z         
2025-03-14T05:00:49.8984104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:49.8984746Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:49.8985066Z         
2025-03-14T05:00:49.8985574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:49.8986220Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:49.8986605Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:49.8986959Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:49.8987286Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:49.8987629Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:49.8987918Z         
2025-03-14T05:00:49.8988314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.8988897Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.8989254Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:49.8989509Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:49.8989885Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.8990248Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:49.8990506Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:49.8990733Z         
2025-03-14T05:00:49.8991187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:49.8991746Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:49.8992036Z         
2025-03-14T05:00:49.8992555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:49.8993240Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:49.8993636Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:49.8993940Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:49.8994263Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:49.8994601Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:49.8994872Z         
2025-03-14T05:00:49.8995465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:49.8996213Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:49.8996568Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:49.8996918Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:49.8997272Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:49.8997574Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:49.8997820Z         
2025-03-14T05:00:49.8998276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:49.8998817Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:49.8999059Z         
2025-03-14T05:00:49.8999195Z 
2025-03-14T05:00:49.8999293Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:49.9000730Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:49.9002129Z         l_stack0_ = L_stack0_
2025-03-14T05:00:49.9002527Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:49.9003117Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:49.9003693Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:49.9004258Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:49.9004747Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9005177Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9005586Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9006010Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9006321Z         
2025-03-14T05:00:49.9006861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:00:49.9007503Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:00:49.9007779Z         
2025-03-14T05:00:49.9008187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:49.9009172Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:49.9009914Z         
2025-03-14T05:00:49.9010334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:49.9011364Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:49.9012131Z         
2025-03-14T05:00:49.9012518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9012991Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9013253Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:49.9013494Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:49.9013774Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9014040Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:49.9014292Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:49.9014544Z         
2025-03-14T05:00:49.9014932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:49.9015898Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9016619Z         
2025-03-14T05:00:49.9017095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:49.9017687Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:49.9017970Z         
2025-03-14T05:00:49.9018381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:49.9018920Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:49.9019215Z         
2025-03-14T05:00:49.9019661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:49.9020194Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:49.9020537Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9020895Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:49.9021172Z         
2025-03-14T05:00:49.9021601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:49.9022126Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:49.9022438Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:49.9022769Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:49.9023047Z         
2025-03-14T05:00:49.9023479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:49.9024024Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9024394Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:49.9024685Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:49.9024956Z         
2025-03-14T05:00:49.9025405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:49.9025974Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:49.9026296Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:49.9026603Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:49.9026866Z         
2025-03-14T05:00:49.9027295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:49.9027837Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:49.9028181Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:49.9028434Z         
2025-03-14T05:00:49.9028844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:49.9029404Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:49.9029746Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:49.9029994Z         
2025-03-14T05:00:49.9030400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:49.9030930Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:49.9031266Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:49.9031513Z         
2025-03-14T05:00:49.9031924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:49.9032490Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:49.9032852Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:49.9033098Z         
2025-03-14T05:00:49.9033558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:49.9034136Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:49.9034403Z         
2025-03-14T05:00:49.9034871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:49.9035451Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:49.9035718Z         
2025-03-14T05:00:49.9036173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:49.9036730Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:49.9037056Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:49.9037401Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:49.9037758Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:49.9038026Z         
2025-03-14T05:00:49.9038472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:49.9039019Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:49.9039344Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:49.9039683Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:49.9040033Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:49.9040300Z         
2025-03-14T05:00:49.9040735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:49.9041255Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:49.9041592Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:49.9041944Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:49.9042204Z         
2025-03-14T05:00:49.9042661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:49.9043179Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:49.9043520Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:49.9043882Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:49.9044145Z         
2025-03-14T05:00:49.9044557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:49.9045032Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:49.9045303Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:49.9045550Z         
2025-03-14T05:00:49.9045978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:49.9046450Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:49.9046718Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:49.9046961Z         
2025-03-14T05:00:49.9047384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:49.9047887Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:49.9048221Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:49.9048486Z         
2025-03-14T05:00:49.9048896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:49.9049392Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:49.9049693Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:49.9049943Z         
2025-03-14T05:00:49.9050397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:49.9050999Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:49.9051312Z         
2025-03-14T05:00:49.9051737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:49.9052300Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:49.9052596Z         
2025-03-14T05:00:49.9053045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:49.9053660Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:49.9054034Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:49.9054330Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:49.9054635Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:49.9054956Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:49.9055226Z         
2025-03-14T05:00:49.9055620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9056211Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9056562Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:49.9056808Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:49.9057176Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9057529Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:49.9057780Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:49.9058003Z         
2025-03-14T05:00:49.9058434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:49.9059005Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:49.9059308Z         
2025-03-14T05:00:49.9059771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:49.9060399Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:49.9060805Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:49.9061109Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:49.9061440Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:49.9061796Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:49.9062068Z         
2025-03-14T05:00:49.9062655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:49.9063382Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:49.9063743Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:49.9064104Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:49.9064544Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:49.9064862Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:49.9065120Z         
2025-03-14T05:00:49.9065593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:49.9066122Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:49.9066372Z         
2025-03-14T05:00:49.9066518Z 
2025-03-14T05:00:49.9066616Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:49.9068056Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:49.9069497Z         l_stack0_ = L_stack0_
2025-03-14T05:00:49.9069917Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:49.9070564Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:49.9071169Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:49.9071772Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:49.9072294Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9072734Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9073156Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9073585Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9073906Z         
2025-03-14T05:00:49.9074482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:00:49.9075185Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:00:49.9075480Z         
2025-03-14T05:00:49.9075933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:49.9076990Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:49.9077777Z         
2025-03-14T05:00:49.9078211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:49.9079321Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:49.9080088Z         
2025-03-14T05:00:49.9080463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9080925Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9081186Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:49.9081576Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:49.9081860Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9082132Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:49.9082387Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:49.9082628Z         
2025-03-14T05:00:49.9083030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:49.9084022Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9084733Z         
2025-03-14T05:00:49.9085252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:49.9085826Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:49.9086101Z         
2025-03-14T05:00:49.9086500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:49.9087025Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:49.9087308Z         
2025-03-14T05:00:49.9087707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:49.9088208Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:49.9088516Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9088833Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:49.9089098Z         
2025-03-14T05:00:49.9089524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:49.9090024Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:49.9090342Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:49.9090689Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:49.9090954Z         
2025-03-14T05:00:49.9091351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:49.9091834Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9092094Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:49.9092352Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:49.9092592Z         
2025-03-14T05:00:49.9092986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:49.9093492Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:49.9093780Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:49.9094045Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:49.9094288Z         
2025-03-14T05:00:49.9094757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:49.9095284Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:49.9095618Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:49.9095859Z         
2025-03-14T05:00:49.9096255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:49.9096774Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:49.9097104Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:49.9097344Z         
2025-03-14T05:00:49.9097736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:49.9098246Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:49.9098597Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:49.9098839Z         
2025-03-14T05:00:49.9099239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:49.9099794Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:49.9100146Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:49.9100384Z         
2025-03-14T05:00:49.9100824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:49.9101367Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:49.9101634Z         
2025-03-14T05:00:49.9102066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:49.9102597Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:49.9102859Z         
2025-03-14T05:00:49.9103339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:49.9103935Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:49.9104354Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:49.9104742Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:49.9105139Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:49.9105443Z         
2025-03-14T05:00:49.9105948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:49.9106541Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:49.9106891Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:49.9107267Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:49.9107665Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:49.9107958Z         
2025-03-14T05:00:49.9108441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:49.9109022Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:49.9109393Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:49.9109781Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:49.9110068Z         
2025-03-14T05:00:49.9110549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:49.9111129Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:49.9111510Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:49.9111911Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:49.9112203Z         
2025-03-14T05:00:49.9112702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:49.9113239Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:49.9113525Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:49.9113765Z         
2025-03-14T05:00:49.9114169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:49.9114631Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:49.9114893Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:49.9115129Z         
2025-03-14T05:00:49.9115524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:49.9116007Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:49.9116299Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:49.9116547Z         
2025-03-14T05:00:49.9116977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:49.9117463Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:49.9117774Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:49.9118023Z         
2025-03-14T05:00:49.9118491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:49.9119089Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:49.9119387Z         
2025-03-14T05:00:49.9119806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:49.9120353Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:49.9120647Z         
2025-03-14T05:00:49.9121092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:49.9121695Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:49.9122061Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:49.9122348Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:49.9122651Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:49.9122969Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:49.9123233Z         
2025-03-14T05:00:49.9123615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9124165Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9124513Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:49.9124756Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:49.9125123Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9125479Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:49.9125724Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:49.9125974Z         
2025-03-14T05:00:49.9126397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:49.9126953Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:49.9127247Z         
2025-03-14T05:00:49.9127694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:49.9128296Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:49.9128663Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:49.9128957Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:49.9129261Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:49.9129581Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:49.9129846Z         
2025-03-14T05:00:49.9130426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:49.9131111Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:49.9131468Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:49.9131824Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:49.9132165Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:49.9132461Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:49.9132705Z         
2025-03-14T05:00:49.9133144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:49.9133664Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:49.9133898Z         
2025-03-14T05:00:49.9134046Z 
2025-03-14T05:00:49.9134135Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:49.9135495Z     def forward(self, L_stack0_: "f32[3231, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:49.9136806Z         l_stack0_ = L_stack0_
2025-03-14T05:00:49.9137196Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:00:49.9137760Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:00:49.9138314Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:00:49.9138878Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:00:49.9139372Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9139764Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9140156Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9140549Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:49.9140843Z         
2025-03-14T05:00:49.9141371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:00:49.9141996Z         mean: "f32[3231, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:00:49.9142263Z         
2025-03-14T05:00:49.9142660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:00:49.9143644Z         scores: "f32[3231, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:00:49.9144510Z         
2025-03-14T05:00:49.9145003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:00:49.9146136Z         proposal_deltas: "f32[3231, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:00:49.9146983Z         
2025-03-14T05:00:49.9147399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9147919Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9148205Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:49.9148460Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:49.9148771Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:49.9149068Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:49.9149343Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:49.9149592Z         
2025-03-14T05:00:49.9150007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:49.9151070Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9151827Z         
2025-03-14T05:00:49.9152294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:49.9152868Z         deltas: "f32[3231, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:00:49.9153143Z         
2025-03-14T05:00:49.9153539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:49.9154086Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:49.9154368Z         
2025-03-14T05:00:49.9154772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:49.9155272Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:49.9155578Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9155901Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:49.9156166Z         
2025-03-14T05:00:49.9156572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:49.9157071Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:49.9157367Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:49.9157684Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:49.9157951Z         
2025-03-14T05:00:49.9158387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:49.9158874Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:49.9159134Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:49.9159410Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:49.9159668Z         
2025-03-14T05:00:49.9160069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:49.9160580Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:49.9160867Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:49.9161129Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:49.9161370Z         
2025-03-14T05:00:49.9161768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:49.9162282Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:49.9162618Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:49.9162860Z         
2025-03-14T05:00:49.9163251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:49.9163757Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:49.9164077Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:49.9164307Z         
2025-03-14T05:00:49.9164685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:49.9165182Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:49.9165497Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:49.9165729Z         
2025-03-14T05:00:49.9166114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:49.9166645Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:49.9166988Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:49.9167372Z         
2025-03-14T05:00:49.9167796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:49.9168329Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:49.9168589Z         
2025-03-14T05:00:49.9169011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:49.9169531Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:49.9169789Z         
2025-03-14T05:00:49.9170221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:49.9170759Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:49.9171074Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:49.9171406Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:49.9171768Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:49.9172030Z         
2025-03-14T05:00:49.9172484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:49.9173034Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:49.9173346Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:49.9173665Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:49.9174004Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:49.9174259Z         
2025-03-14T05:00:49.9174682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:49.9175187Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:49.9175515Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:49.9175856Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:49.9176107Z         
2025-03-14T05:00:49.9176523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:49.9177021Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:49.9177350Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:49.9177697Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:49.9177951Z         
2025-03-14T05:00:49.9178353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:49.9178816Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:49.9179077Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:49.9179312Z         
2025-03-14T05:00:49.9179704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:49.9180198Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:49.9180460Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:49.9180696Z         
2025-03-14T05:00:49.9181092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:49.9181762Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:49.9182060Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:49.9182317Z         
2025-03-14T05:00:49.9182706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:49.9183183Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:49.9183474Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:49.9183724Z         
2025-03-14T05:00:49.9184233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:49.9184913Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:49.9185226Z         
2025-03-14T05:00:49.9185709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:49.9186305Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:49.9186593Z         
2025-03-14T05:00:49.9187044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:49.9187658Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:49.9188034Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:49.9188329Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:49.9188633Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:49.9188952Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:49.9189214Z         
2025-03-14T05:00:49.9189595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:49.9190152Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9190500Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:49.9190745Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:49.9191110Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:49.9191465Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:49.9191713Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:49.9191937Z         
2025-03-14T05:00:49.9192363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:49.9192925Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:00:49.9193217Z         
2025-03-14T05:00:49.9193660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:49.9194295Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:49.9194652Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:49.9194942Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:49.9195239Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:49.9195550Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:49.9195809Z         
2025-03-14T05:00:49.9196366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:49.9197043Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:49.9197385Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:49.9197719Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:49.9198063Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:49.9198381Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:49.9198626Z         
2025-03-14T05:00:49.9199080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:49.9199617Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:49.9199855Z         
2025-03-14T05:00:51.5588539Z 
2025-03-14T05:00:51.5589485Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.5591315Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:51.5592811Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.5593176Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.5593680Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5594385Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5595092Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5595805Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5596316Z         
2025-03-14T05:00:51.5597004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5597812Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5598243Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.5598623Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.5599081Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5599536Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:51.5599939Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.5600290Z         
2025-03-14T05:00:51.5600947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.5602756Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5604575Z         
2025-03-14T05:00:51.5605423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.5606509Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.5606985Z         
2025-03-14T05:00:51.5607730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.5608716Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.5609209Z         
2025-03-14T05:00:51.5609966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.5610904Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.5611579Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5612138Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:51.5612591Z         
2025-03-14T05:00:51.5613385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.5614370Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.5614895Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.5615465Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:51.5615919Z         
2025-03-14T05:00:51.5616645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.5617557Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5617991Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.5618435Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:51.5618841Z         
2025-03-14T05:00:51.5619567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.5620732Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.5621350Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.5621912Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:51.5622436Z         
2025-03-14T05:00:51.5623399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.5624665Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.5625259Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:51.5625668Z         
2025-03-14T05:00:51.5626337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.5627215Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.5627885Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:51.5628275Z         
2025-03-14T05:00:51.5628978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.5630035Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.5630555Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:51.5630923Z         
2025-03-14T05:00:51.5631576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.5632541Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.5633161Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:51.5633573Z         
2025-03-14T05:00:51.5634312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.5635297Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.5635734Z         
2025-03-14T05:00:51.5636613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.5637646Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.5638130Z         
2025-03-14T05:00:51.5638888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.5639874Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.5640375Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:51.5640932Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.5641531Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:51.5641955Z         
2025-03-14T05:00:51.5642706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.5643532Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.5644052Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:51.5644563Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.5645120Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:51.5645540Z         
2025-03-14T05:00:51.5646298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.5647204Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.5647752Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.5648315Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:51.5648741Z         
2025-03-14T05:00:51.5649517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.5650449Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.5651133Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.5651755Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:51.5652194Z         
2025-03-14T05:00:51.5652945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.5653811Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.5654283Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.5654702Z         
2025-03-14T05:00:51.5655464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.5656333Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.5656800Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.5657222Z         
2025-03-14T05:00:51.5657976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.5658945Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.5659473Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.5659911Z         
2025-03-14T05:00:51.5660678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.5661610Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.5662124Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.5662522Z         
2025-03-14T05:00:51.5663276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.5664482Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.5665039Z         
2025-03-14T05:00:51.5665821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.5666893Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:51.5667396Z         
2025-03-14T05:00:51.5668226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.5669420Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:51.5670111Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.5670639Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:51.5671167Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:51.5671702Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:51.5672128Z         
2025-03-14T05:00:51.5672793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5673749Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5674320Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:51.5674815Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:51.5675463Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5676081Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:51.5676483Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:51.5676853Z         
2025-03-14T05:00:51.5677594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.5678662Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.5679216Z         
2025-03-14T05:00:51.5680005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.5681113Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:51.5682048Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.5682558Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:51.5683300Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:51.5683884Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:51.5684319Z         
2025-03-14T05:00:51.5685435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.5686814Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.5687417Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.5688033Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.5688640Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.5689168Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.5689590Z         
2025-03-14T05:00:51.5690382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.5691330Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.5691733Z         
2025-03-14T05:00:51.5691982Z 
2025-03-14T05:00:51.5692119Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.5693592Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:51.5695056Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.5695443Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.5696004Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5696762Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5697486Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5698210Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5698791Z         
2025-03-14T05:00:51.5699500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5700351Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5700768Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.5701157Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.5701627Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5702096Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:51.5702518Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.5702899Z         
2025-03-14T05:00:51.5703563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.5705558Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5706965Z         
2025-03-14T05:00:51.5707876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.5708962Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.5709438Z         
2025-03-14T05:00:51.5710197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.5711110Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.5711538Z         
2025-03-14T05:00:51.5712191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.5713038Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.5713544Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5714045Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:51.5714445Z         
2025-03-14T05:00:51.5715144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.5716016Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.5716552Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.5717093Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:51.5717518Z         
2025-03-14T05:00:51.5718181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.5719026Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5719478Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.5719907Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:51.5720281Z         
2025-03-14T05:00:51.5720909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.5721691Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.5722120Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.5722611Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:51.5722991Z         
2025-03-14T05:00:51.5723625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.5724504Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.5725008Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:51.5725352Z         
2025-03-14T05:00:51.5725979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.5726853Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.5727373Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:51.5727741Z         
2025-03-14T05:00:51.5728384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.5729250Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.5729847Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:51.5730216Z         
2025-03-14T05:00:51.5730955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.5731885Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.5732420Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:51.5732786Z         
2025-03-14T05:00:51.5733476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.5734405Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.5734847Z         
2025-03-14T05:00:51.5735577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.5736447Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.5736842Z         
2025-03-14T05:00:51.5737550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.5738481Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.5738983Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:51.5739518Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.5740080Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:51.5740497Z         
2025-03-14T05:00:51.5741226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.5742127Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.5742624Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:51.5743161Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.5743796Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:51.5744365Z         
2025-03-14T05:00:51.5745123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.5746011Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.5746527Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.5747091Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:51.5747507Z         
2025-03-14T05:00:51.5748244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.5749139Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.5749712Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.5750329Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:51.5750753Z         
2025-03-14T05:00:51.5751469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.5752245Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.5752691Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.5753103Z         
2025-03-14T05:00:51.5753787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.5754580Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.5754979Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.5755354Z         
2025-03-14T05:00:51.5756021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.5756806Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.5757292Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.5757688Z         
2025-03-14T05:00:51.5758392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.5759251Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.5759728Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.5760137Z         
2025-03-14T05:00:51.5760877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.5761868Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.5762372Z         
2025-03-14T05:00:51.5763079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.5764045Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:51.5764511Z         
2025-03-14T05:00:51.5765247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.5766175Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:51.5766864Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.5767306Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:51.5767825Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:51.5768352Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:51.5768794Z         
2025-03-14T05:00:51.5769444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5770423Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5771017Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:51.5771421Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:51.5772047Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5772655Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:51.5773060Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:51.5773419Z         
2025-03-14T05:00:51.5774281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.5775398Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.5776017Z         
2025-03-14T05:00:51.5776771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.5777877Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:51.5778518Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.5779017Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:51.5779529Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:51.5780068Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:51.5780547Z         
2025-03-14T05:00:51.5781791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.5783098Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.5783686Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.5784385Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.5785010Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.5785528Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.5785939Z         
2025-03-14T05:00:51.5786761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.5787723Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.5788130Z         
2025-03-14T05:00:51.5788366Z 
2025-03-14T05:00:51.5788502Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:51.5789719Z     def forward(self, L_predictions_0_: "f32[3231, 81][81, 1]cpu", L_predictions_1_: "f32[3231, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1231 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1231 - s0, 4][4, 1]cpu"):
2025-03-14T05:00:51.5791326Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:00:51.5791684Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:00:51.5792207Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5792901Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5793577Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5794253Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:00:51.5794785Z         
2025-03-14T05:00:51.5795425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5796213Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5796615Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:00:51.5796980Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:00:51.5797533Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:00:51.5797943Z         getitem_2: "Sym(1231 - s0)" = size_1[0]
2025-03-14T05:00:51.5798374Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:00:51.5798732Z         
2025-03-14T05:00:51.5799416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:00:51.5801052Z         proposal_boxes: "f32[3231, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5802359Z         
2025-03-14T05:00:51.5803188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:00:51.5804179Z         deltas: "f32[3231, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:00:51.5804610Z         
2025-03-14T05:00:51.5805278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:00:51.5806166Z         boxes: "f32[3231, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:00:51.5806632Z         
2025-03-14T05:00:51.5807286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:00:51.5808134Z         getitem_4: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:00:51.5808635Z         getitem_5: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5809184Z         widths: "f32[3231][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:00:51.5809620Z         
2025-03-14T05:00:51.5810324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:00:51.5811190Z         getitem_6: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:00:51.5811687Z         getitem_7: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:00:51.5812226Z         heights: "f32[3231][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:00:51.5812743Z         
2025-03-14T05:00:51.5813443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:00:51.5814320Z         getitem_8: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:00:51.5814757Z         mul: "f32[3231][1]cpu" = 0.5 * widths
2025-03-14T05:00:51.5815180Z         ctr_x: "f32[3231][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:00:51.5815569Z         
2025-03-14T05:00:51.5816268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:00:51.5817190Z         getitem_9: "f32[3231][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:00:51.5817663Z         mul_1: "f32[3231][1]cpu" = 0.5 * heights
2025-03-14T05:00:51.5818098Z         ctr_y: "f32[3231][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:00:51.5818505Z         
2025-03-14T05:00:51.5819235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:00:51.5820210Z         getitem_10: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:00:51.5820771Z         dx: "f32[3231, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:00:51.5821158Z         
2025-03-14T05:00:51.5821880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:00:51.5822835Z         getitem_11: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:00:51.5823401Z         dy: "f32[3231, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:00:51.5823790Z         
2025-03-14T05:00:51.5824584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:00:51.5825503Z         getitem_12: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:00:51.5826070Z         dw: "f32[3231, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:00:51.5826466Z         
2025-03-14T05:00:51.5827174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:00:51.5828178Z         getitem_13: "f32[3231, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:00:51.5828673Z         dh: "f32[3231, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:00:51.5828981Z         
2025-03-14T05:00:51.5829682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:00:51.5830629Z         dw_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:00:51.5831053Z         
2025-03-14T05:00:51.5831812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:00:51.5832752Z         dh_1: "f32[3231, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:00:51.5833186Z         
2025-03-14T05:00:51.5833957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:00:51.5834922Z         getitem_14: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:00:51.5835555Z         mul_2: "f32[3231, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:00:51.5836096Z         getitem_15: "f32[3231, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:00:51.5836673Z         pred_ctr_x: "f32[3231, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:00:51.5837080Z         
2025-03-14T05:00:51.5837834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:00:51.5838758Z         getitem_16: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:00:51.5839278Z         mul_3: "f32[3231, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:00:51.5839819Z         getitem_17: "f32[3231, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:00:51.5840377Z         pred_ctr_y: "f32[3231, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:00:51.5840784Z         
2025-03-14T05:00:51.5841483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:00:51.5842323Z         exp: "f32[3231, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:00:51.5842893Z         getitem_18: "f32[3231, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:00:51.5843458Z         pred_w: "f32[3231, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:00:51.5843902Z         
2025-03-14T05:00:51.5844648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:00:51.5845550Z         exp_1: "f32[3231, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:00:51.5846093Z         getitem_19: "f32[3231, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:00:51.5846669Z         pred_h: "f32[3231, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:00:51.5847069Z         
2025-03-14T05:00:51.5847752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:00:51.5848558Z         mul_6: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:00:51.5848980Z         x1: "f32[3231, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:00:51.5849368Z         
2025-03-14T05:00:51.5850038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:00:51.5850836Z         mul_7: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:00:51.5851259Z         y1: "f32[3231, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:00:51.5851627Z         
2025-03-14T05:00:51.5852305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:00:51.5853121Z         mul_8: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:00:51.5853615Z         x2: "f32[3231, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:00:51.5854025Z         
2025-03-14T05:00:51.5854720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:00:51.5855539Z         mul_9: "f32[3231, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:00:51.5856008Z         y2: "f32[3231, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:00:51.5856406Z         
2025-03-14T05:00:51.5857175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:00:51.5858328Z         pred_boxes: "f32[3231, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:00:51.5858784Z         
2025-03-14T05:00:51.5859697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:00:51.5860716Z         predict_boxes: "f32[3231, 320][320, 1]cpu" = pred_boxes.reshape((3231, 320));  pred_boxes = None
2025-03-14T05:00:51.5861186Z         
2025-03-14T05:00:51.5861963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:00:51.5863019Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:00:51.5863607Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:00:51.5864069Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:00:51.5864735Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:00:51.5865357Z         getitem_23: "f32[1231 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:00:51.5865777Z         
2025-03-14T05:00:51.5866474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:00:51.5867526Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5868127Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:00:51.5868544Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:00:51.5869215Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:00:51.5869846Z         getitem_26: "Sym(1231 - s0)" = size_3[0]
2025-03-14T05:00:51.5870261Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:00:51.5870604Z         
2025-03-14T05:00:51.5871352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:00:51.5872448Z         probs: "f32[3231, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:00:51.5873011Z         
2025-03-14T05:00:51.5873847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:00:51.5874842Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:00:51.5875454Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:00:51.5875948Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:00:51.5876459Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:00:51.5876991Z         getitem_31: "f32[1231 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:00:51.5877422Z         
2025-03-14T05:00:51.5878400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:51.5879647Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:00:51.5880237Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:51.5880908Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:00:51.5881744Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:51.5882260Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:51.5882647Z         
2025-03-14T05:00:51.5883452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:51.5884384Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:51.5884774Z         
2025-03-14T05:00:53.9731368Z 
2025-03-14T05:00:53.9737615Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:53.9739936Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:00:53.9740363Z         l_scores_0_ = L_scores_0_
2025-03-14T05:00:53.9740659Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:00:53.9740891Z         
2025-03-14T05:00:53.9741594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:53.9742819Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:00:53.9743225Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:53.9743708Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:00:53.9744303Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:53.9744653Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:53.9744930Z         
2025-03-14T05:00:53.9745457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:53.9746086Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:53.9746347Z         
2025-03-14T05:00:53.9746457Z 
2025-03-14T05:00:53.9746566Z class GraphModule(torch.nn.Module):
2025-03-14T05:00:53.9746894Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:00:53.9747211Z         l_scores_0_ = L_scores_0_
2025-03-14T05:00:53.9747433Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:00:53.9747637Z         
2025-03-14T05:00:53.9748225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:00:53.9748929Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:00:53.9749270Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:00:53.9749607Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:00:53.9749944Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:00:53.9750257Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:00:53.9750515Z         
2025-03-14T05:00:53.9751646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:00:53.9752218Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:00:53.9752476Z         
2025-03-14T05:01:11.8857204Z Compilation time (from dynamo_timed): 35.796977801
2025-03-14T05:01:11.8857766Z pass
2025-03-14T05:01:11.8862370Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:01:11.8867592Z TIMING: entire_frame_compile:35.79698 gc:0.0383 _recursive_pre_grad_passes:0.02988 async_compile.wait:9.42249 backend_compile:24.36149 _recursive_joint_graph_passes:0.17904 _recursive_post_grad_passes:0.08963 code_gen:12.55033 inductor_compile:14.40697 total_wall_time:35.79698
2025-03-14T05:01:11.8873024Z STATS: call_* op count: 607 | FakeTensorMode.__torch_dispatch__:17714 | FakeTensor.__torch_dispatch__:1777 | ProxyTorchDispatchMode.__torch_dispatch__:5620 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T05:01:11.8874458Z Dynamo produced 53 graphs covering 607 ops with 42 graph breaks (6 unique)
2025-03-14T05:01:17.4355639Z 
2025-03-14T05:01:27.3815323Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:01:27.3820811Z loading model: 0it [00:09, ?it/s]
2025-03-14T05:01:27.3823455Z cpu  eval  detectron2_fasterrcnn_r_50_dc5     
2025-03-14T05:01:41.0803767Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_dc5. Setting accuracy check to cosine
2025-03-14T05:01:41.1167700Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:01:57.3088198Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:02:11.3082218Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:02:21.5006124Z 
2025-03-14T05:02:21.5010019Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:21.5072790Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:02:21.5125596Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:02:21.5126134Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5126919Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5127721Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5128500Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5129245Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5129965Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5130815Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5131682Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5132503Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5133260Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5133968Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5134744Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5135612Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5136476Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5137281Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5137985Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5138719Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5139511Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5140271Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5141007Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5141722Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5142470Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5143304Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5144092Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5144938Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5145673Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5146402Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5147193Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5147943Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5148676Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5149362Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5150118Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5150927Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5151703Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5152442Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5153142Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5153853Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5154594Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5155317Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5156015Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5156682Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5157377Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5158124Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5158852Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5159554Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5160236Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5160928Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5161682Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5162412Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5163109Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5163778Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5164489Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5165239Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5165983Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5166676Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5167333Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5168020Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5168762Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5169486Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5170184Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5170893Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5171573Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5172313Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5173033Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5173740Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5174400Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5175088Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5175829Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5176542Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5177237Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5177926Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5178653Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5179438Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5180189Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5180911Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5181697Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5182404Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5183192Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5183959Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5184699Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5185382Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5186124Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5186891Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5187696Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5188423Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5189112Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5189836Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5190609Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5191363Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5192115Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5192811Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5193573Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5194366Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5195119Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5195831Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5196490Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5197177Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5197905Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5198621Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5199312Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5199967Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5200648Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5201374Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5202111Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5202801Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5203458Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5204142Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5204872Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5205597Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5206301Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5206983Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5207710Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5208444Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5209165Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5209854Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5210507Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5211183Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5211920Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5212632Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5213313Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5213966Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5214646Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5215406Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5216121Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5216813Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5217470Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5218163Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5218933Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5219705Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5220475Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5221191Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5221921Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5222703Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5223473Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5224308Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5225040Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5225847Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5226658Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5227455Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5228239Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5228947Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5229685Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5230526Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5231288Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5232027Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5232725Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5233467Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5234253Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5235050Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5235796Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5236511Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5237261Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5238041Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5238796Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5239526Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5240220Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5240953Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5241738Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5242491Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5243188Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5243848Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5245484Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5246263Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5246997Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5247697Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5248371Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5249103Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5249880Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5250630Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5251374Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5252063Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5252786Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5253541Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5254259Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5254964Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5255661Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5256340Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5257110Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5257869Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5258596Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5259318Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5260052Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5260835Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5261599Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5262339Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5263035Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5263821Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5264720Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5265551Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5266300Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5266991Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5267717Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5268511Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5269315Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5270084Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5270816Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5271590Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5272368Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5273141Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5273936Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5274724Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5275524Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5276373Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5277192Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5277991Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5278757Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5279576Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5280466Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5281314Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5282247Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5283007Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5283804Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5284651Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5285493Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5286281Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5287034Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5287825Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5288685Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5289508Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5290378Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5291098Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5291822Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5292599Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5293355Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5294081Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5294804Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5295550Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5296336Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5297133Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5297865Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5298577Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5299334Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5300137Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5300925Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5301686Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5302393Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5303152Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5303982Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5304869Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5305718Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5306441Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5307244Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5308107Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5308957Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5309760Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5310557Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5311382Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5312262Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5313103Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5313835Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5314486Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5315168Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5315940Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5316652Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5317338Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5317991Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5318669Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5319446Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5320189Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5320873Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5321521Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5322200Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5322927Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5323634Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5324341Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5325087Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:02:21.5325829Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:02:21.5326521Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:02:21.5327252Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:02:21.5328043Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:02:21.5328847Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:02:21.5329600Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:02:21.5330075Z         
2025-03-14T05:02:21.5330481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5331289Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5331886Z         
2025-03-14T05:02:21.5332263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5334024Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5335598Z         
2025-03-14T05:02:21.5335983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:02:21.5336466Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:02:21.5336730Z         
2025-03-14T05:02:21.5337187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:02:21.5337835Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:02:21.5338195Z         
2025-03-14T05:02:21.5338557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5339305Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5339864Z         
2025-03-14T05:02:21.5340225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5342083Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5343715Z         
2025-03-14T05:02:21.5344197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5344777Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:02:21.5345080Z         
2025-03-14T05:02:21.5345471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5346261Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5346839Z         
2025-03-14T05:02:21.5347200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5349188Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5350953Z         
2025-03-14T05:02:21.5351349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5351876Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:02:21.5352168Z         
2025-03-14T05:02:21.5352536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5353356Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5353956Z         
2025-03-14T05:02:21.5354344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5356344Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5357985Z         
2025-03-14T05:02:21.5358320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5359064Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.5359617Z         
2025-03-14T05:02:21.5359964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5361852Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5363559Z         
2025-03-14T05:02:21.5363931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5364415Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:02:21.5364687Z         
2025-03-14T05:02:21.5365064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5365563Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:02:21.5365840Z         
2025-03-14T05:02:21.5366180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5366937Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5367476Z         
2025-03-14T05:02:21.5367843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5369710Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5371335Z         
2025-03-14T05:02:21.5371718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5372211Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:02:21.5372482Z         
2025-03-14T05:02:21.5372829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5373574Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5374125Z         
2025-03-14T05:02:21.5374486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5376331Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5377965Z         
2025-03-14T05:02:21.5378344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5378832Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:02:21.5379097Z         
2025-03-14T05:02:21.5379438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5380186Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5380744Z         
2025-03-14T05:02:21.5381115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5383310Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5385220Z         
2025-03-14T05:02:21.5385637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5386189Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:02:21.5386498Z         
2025-03-14T05:02:21.5386919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5387466Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:02:21.5387772Z         
2025-03-14T05:02:21.5388153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5388978Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5389582Z         
2025-03-14T05:02:21.5389974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5392054Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5393837Z         
2025-03-14T05:02:21.5394234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5394743Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:02:21.5395022Z         
2025-03-14T05:02:21.5395375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5396186Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5396756Z         
2025-03-14T05:02:21.5397155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5399162Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5400899Z         
2025-03-14T05:02:21.5401303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5401819Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:02:21.5402102Z         
2025-03-14T05:02:21.5402461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5403262Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5403821Z         
2025-03-14T05:02:21.5404182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5406038Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5407701Z         
2025-03-14T05:02:21.5408073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5408570Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:02:21.5408846Z         
2025-03-14T05:02:21.5409220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5409714Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:02:21.5409989Z         
2025-03-14T05:02:21.5410328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5411085Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5411643Z         
2025-03-14T05:02:21.5411998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5413821Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5415447Z         
2025-03-14T05:02:21.5415820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5416309Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:02:21.5416577Z         
2025-03-14T05:02:21.5416917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5417657Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5418204Z         
2025-03-14T05:02:21.5418552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5420441Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5422072Z         
2025-03-14T05:02:21.5422446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5422935Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:02:21.5423206Z         
2025-03-14T05:02:21.5423545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5424431Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5425050Z         
2025-03-14T05:02:21.5425460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5427506Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5429310Z         
2025-03-14T05:02:21.5429714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5430580Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.5431228Z         
2025-03-14T05:02:21.5431623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5433729Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5435501Z         
2025-03-14T05:02:21.5435874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5436361Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:02:21.5436631Z         
2025-03-14T05:02:21.5437011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5437512Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:02:21.5437787Z         
2025-03-14T05:02:21.5438128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5438880Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5439426Z         
2025-03-14T05:02:21.5439777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5441636Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5443283Z         
2025-03-14T05:02:21.5443662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5444150Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:02:21.5444415Z         
2025-03-14T05:02:21.5444757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5445496Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5446041Z         
2025-03-14T05:02:21.5446397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5448254Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5449919Z         
2025-03-14T05:02:21.5450292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5450785Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:02:21.5451056Z         
2025-03-14T05:02:21.5451397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5452141Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5452696Z         
2025-03-14T05:02:21.5453072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5454942Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5456568Z         
2025-03-14T05:02:21.5456939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5457429Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:02:21.5457706Z         
2025-03-14T05:02:21.5458067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5458558Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:02:21.5458830Z         
2025-03-14T05:02:21.5459168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5459901Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5460438Z         
2025-03-14T05:02:21.5460789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5462635Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5464394Z         
2025-03-14T05:02:21.5464823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5465372Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:02:21.5465656Z         
2025-03-14T05:02:21.5466021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5466781Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5467379Z         
2025-03-14T05:02:21.5467796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5469865Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5471666Z         
2025-03-14T05:02:21.5472085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5472625Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:02:21.5472916Z         
2025-03-14T05:02:21.5473252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5473993Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5474577Z         
2025-03-14T05:02:21.5474952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5476820Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5478461Z         
2025-03-14T05:02:21.5478832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5479327Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:02:21.5479604Z         
2025-03-14T05:02:21.5479973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5480464Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:02:21.5480737Z         
2025-03-14T05:02:21.5481073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5482004Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5482632Z         
2025-03-14T05:02:21.5483031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5484936Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5486574Z         
2025-03-14T05:02:21.5486984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5487474Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:02:21.5487745Z         
2025-03-14T05:02:21.5488090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5488828Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5489376Z         
2025-03-14T05:02:21.5489732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5491577Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5493255Z         
2025-03-14T05:02:21.5493619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5494092Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:02:21.5494353Z         
2025-03-14T05:02:21.5494686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5495419Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5495956Z         
2025-03-14T05:02:21.5496312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5498136Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5499738Z         
2025-03-14T05:02:21.5500098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5500585Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:02:21.5500867Z         
2025-03-14T05:02:21.5501240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5501736Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:02:21.5502010Z         
2025-03-14T05:02:21.5502355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5503088Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5503626Z         
2025-03-14T05:02:21.5504000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5506092Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5507671Z         
2025-03-14T05:02:21.5508043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5508520Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:02:21.5508788Z         
2025-03-14T05:02:21.5509128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5509886Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5510452Z         
2025-03-14T05:02:21.5510800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5512683Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5514336Z         
2025-03-14T05:02:21.5514721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5515217Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:02:21.5515476Z         
2025-03-14T05:02:21.5515812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5516574Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5517127Z         
2025-03-14T05:02:21.5517485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5519330Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5520961Z         
2025-03-14T05:02:21.5521299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5522037Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.5522600Z         
2025-03-14T05:02:21.5522968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5524863Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5526565Z         
2025-03-14T05:02:21.5526953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5527454Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:02:21.5527728Z         
2025-03-14T05:02:21.5528117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5528606Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:02:21.5528891Z         
2025-03-14T05:02:21.5529247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5530011Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5530564Z         
2025-03-14T05:02:21.5530932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5532882Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5534651Z         
2025-03-14T05:02:21.5535048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5535552Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:02:21.5535834Z         
2025-03-14T05:02:21.5536209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5536998Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5537589Z         
2025-03-14T05:02:21.5537983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5540040Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5541864Z         
2025-03-14T05:02:21.5542294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5542834Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:02:21.5543119Z         
2025-03-14T05:02:21.5543493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5544378Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5544992Z         
2025-03-14T05:02:21.5545390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5547449Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5549278Z         
2025-03-14T05:02:21.5549696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5550237Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:02:21.5550536Z         
2025-03-14T05:02:21.5550946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5551485Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:02:21.5551780Z         
2025-03-14T05:02:21.5552157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5552982Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5553577Z         
2025-03-14T05:02:21.5553985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5555966Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5557589Z         
2025-03-14T05:02:21.5557964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5558451Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:02:21.5558717Z         
2025-03-14T05:02:21.5559055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5559786Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5560330Z         
2025-03-14T05:02:21.5560688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5562503Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5564122Z         
2025-03-14T05:02:21.5564501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5564977Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:02:21.5565236Z         
2025-03-14T05:02:21.5565572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5566310Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5566901Z         
2025-03-14T05:02:21.5567276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5569209Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5570929Z         
2025-03-14T05:02:21.5571327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5571833Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:02:21.5572113Z         
2025-03-14T05:02:21.5572514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5573034Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:02:21.5573302Z         
2025-03-14T05:02:21.5573658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5574435Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5575016Z         
2025-03-14T05:02:21.5575377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5577329Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5579081Z         
2025-03-14T05:02:21.5579487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5580009Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:02:21.5580287Z         
2025-03-14T05:02:21.5580647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5581540Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5582157Z         
2025-03-14T05:02:21.5582598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5584641Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5586456Z         
2025-03-14T05:02:21.5586859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5587379Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:02:21.5587656Z         
2025-03-14T05:02:21.5588018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5588801Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5589372Z         
2025-03-14T05:02:21.5589742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5591682Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5593385Z         
2025-03-14T05:02:21.5593770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5594275Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:02:21.5594557Z         
2025-03-14T05:02:21.5594942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5595449Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:02:21.5595726Z         
2025-03-14T05:02:21.5596104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5596888Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5597472Z         
2025-03-14T05:02:21.5597849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5599794Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5601429Z         
2025-03-14T05:02:21.5601806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5602289Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:02:21.5602552Z         
2025-03-14T05:02:21.5602902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5603672Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5604219Z         
2025-03-14T05:02:21.5604576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5606394Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5608031Z         
2025-03-14T05:02:21.5608407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5608911Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:02:21.5609186Z         
2025-03-14T05:02:21.5609523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5610269Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5610807Z         
2025-03-14T05:02:21.5611183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5613030Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5614641Z         
2025-03-14T05:02:21.5615011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5615495Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:02:21.5615763Z         
2025-03-14T05:02:21.5616137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5616622Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:02:21.5616885Z         
2025-03-14T05:02:21.5617234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5617990Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5618552Z         
2025-03-14T05:02:21.5618926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5620894Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5622617Z         
2025-03-14T05:02:21.5623014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5623520Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:02:21.5623795Z         
2025-03-14T05:02:21.5624239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5625086Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5625693Z         
2025-03-14T05:02:21.5626074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5628014Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5629720Z         
2025-03-14T05:02:21.5630123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5630636Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:02:21.5630917Z         
2025-03-14T05:02:21.5631281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5632060Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5632610Z         
2025-03-14T05:02:21.5632969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5634800Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5636433Z         
2025-03-14T05:02:21.5636802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5637280Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:02:21.5637551Z         
2025-03-14T05:02:21.5637947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5638430Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:02:21.5638713Z         
2025-03-14T05:02:21.5639055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5639796Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5640346Z         
2025-03-14T05:02:21.5640697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5642525Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5644142Z         
2025-03-14T05:02:21.5644510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5644990Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:02:21.5645251Z         
2025-03-14T05:02:21.5645589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5646324Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5646867Z         
2025-03-14T05:02:21.5647218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5649047Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5650683Z         
2025-03-14T05:02:21.5651054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5651530Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:02:21.5651788Z         
2025-03-14T05:02:21.5671110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5672295Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5672935Z         
2025-03-14T05:02:21.5673334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5675253Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5676910Z         
2025-03-14T05:02:21.5677283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5678063Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.5678635Z         
2025-03-14T05:02:21.5679007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5680955Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5682879Z         
2025-03-14T05:02:21.5683295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5683823Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:02:21.5684117Z         
2025-03-14T05:02:21.5684526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5685060Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:02:21.5685355Z         
2025-03-14T05:02:21.5685729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5686587Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5687156Z         
2025-03-14T05:02:21.5687566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5689532Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5691240Z         
2025-03-14T05:02:21.5691642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5692157Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:02:21.5692421Z         
2025-03-14T05:02:21.5692761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5693492Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5694032Z         
2025-03-14T05:02:21.5694389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5696199Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5697855Z         
2025-03-14T05:02:21.5698233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5698719Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:02:21.5698972Z         
2025-03-14T05:02:21.5699313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5700075Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5700636Z         
2025-03-14T05:02:21.5700986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5702882Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5704632Z         
2025-03-14T05:02:21.5705035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5705563Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:02:21.5705846Z         
2025-03-14T05:02:21.5706224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5706723Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:02:21.5706999Z         
2025-03-14T05:02:21.5707345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5708085Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.5708627Z         
2025-03-14T05:02:21.5708986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5710842Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5712472Z         
2025-03-14T05:02:21.5712853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5713341Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:02:21.5713607Z         
2025-03-14T05:02:21.5713948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5714693Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.5715256Z         
2025-03-14T05:02:21.5715618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5717462Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5719044Z         
2025-03-14T05:02:21.5719411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5719887Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:02:21.5720147Z         
2025-03-14T05:02:21.5720482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5721198Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.5721735Z         
2025-03-14T05:02:21.5722082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.5723864Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.5725450Z         
2025-03-14T05:02:21.5725815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.5726303Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:02:21.5726572Z         
2025-03-14T05:02:21.5726944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.5727425Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:02:21.5727694Z         
2025-03-14T05:02:21.5728244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.5728915Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.5729248Z         
2025-03-14T05:02:21.5729640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.5730130Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:21.5730391Z         
2025-03-14T05:02:21.5730922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.5731559Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.5731834Z         
2025-03-14T05:02:21.5732218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.5732705Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:02:21.5732969Z         
2025-03-14T05:02:21.5733432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:02:21.5734055Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:02:21.5734392Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:02:21.5734670Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:02:21.5734907Z         
2025-03-14T05:02:21.5735326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:02:21.5735842Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:02:21.5736088Z         
2025-03-14T05:02:21.5736505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:02:21.5737007Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:02:21.5737280Z         
2025-03-14T05:02:21.5737743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:02:21.5738380Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:02:21.5738707Z         
2025-03-14T05:02:21.5739214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:02:21.5739806Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:02:21.5740412Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:02:21.5741016Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:02:21.5741312Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:02:21.5741549Z         
2025-03-14T05:02:21.5741963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.5742445Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T05:02:21.5742712Z         
2025-03-14T05:02:21.5743066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.5744221Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:02:21.5745140Z         
2025-03-14T05:02:21.5745517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:02:21.5746062Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T05:02:21.5746392Z         
2025-03-14T05:02:21.5746873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:02:21.5748127Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:02:21.5749083Z         
2025-03-14T05:02:21.5749531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:02:21.5750760Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:02:21.5751712Z         
2025-03-14T05:02:21.5752133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:02:21.5752679Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:02:21.5753027Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:02:21.5753292Z         
2025-03-14T05:02:21.5753800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:02:21.5754426Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T05:02:21.5754812Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:02:21.5755207Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:02:21.5755514Z         
2025-03-14T05:02:21.5756024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:21.5756687Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:02:21.5757025Z         
2025-03-14T05:02:21.5757551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:21.5758192Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:02:21.5758552Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:21.5758901Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:21.5759169Z         
2025-03-14T05:02:21.5759643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:21.5760244Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:02:21.5760539Z         
2025-03-14T05:02:21.5760946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:21.5761459Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:21.5761727Z         
2025-03-14T05:02:21.5762138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:21.5762647Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:21.5762964Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.5763296Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:21.5763570Z         
2025-03-14T05:02:21.5763982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:21.5764479Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:21.5764805Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:21.5765131Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:21.5765400Z         
2025-03-14T05:02:21.5765805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:21.5766296Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.5766570Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:21.5766830Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:02:21.5767079Z         
2025-03-14T05:02:21.5767486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:21.5768003Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:21.5768296Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:21.5768572Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:02:21.5768820Z         
2025-03-14T05:02:21.5769296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:21.5769837Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:21.5770183Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:21.5770421Z         
2025-03-14T05:02:21.5770815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:21.5771323Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:21.5771652Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:21.5771891Z         
2025-03-14T05:02:21.5772282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:21.5772789Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:21.5773112Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:02:21.5773349Z         
2025-03-14T05:02:21.5773743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:21.5774277Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:21.5774626Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:02:21.5774866Z         
2025-03-14T05:02:21.5775310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:21.5775865Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:21.5776135Z         
2025-03-14T05:02:21.5776559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:21.5777100Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:21.5777360Z         
2025-03-14T05:02:21.5777801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:21.5778364Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:21.5778690Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:02:21.5779032Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:21.5779386Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:02:21.5779652Z         
2025-03-14T05:02:21.5780097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:21.5780655Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:21.5780988Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:02:21.5781335Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:21.5781863Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:02:21.5782139Z         
2025-03-14T05:02:21.5782634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:21.5783165Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:21.5783537Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:21.5783942Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:02:21.5784259Z         
2025-03-14T05:02:21.5784710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:21.5785282Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:21.5785637Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:21.5786017Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:02:21.5786278Z         
2025-03-14T05:02:21.5786690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:21.5787159Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:21.5787423Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:21.5787667Z         
2025-03-14T05:02:21.5788075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:21.5788545Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:21.5788820Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:21.5789067Z         
2025-03-14T05:02:21.5789467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:21.5789961Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:21.5790264Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:21.5790522Z         
2025-03-14T05:02:21.5790923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:21.5791406Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:21.5791739Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:21.5791992Z         
2025-03-14T05:02:21.5792438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:21.5793033Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:21.5793338Z         
2025-03-14T05:02:21.5793769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:21.5794328Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:21.5794617Z         
2025-03-14T05:02:21.5795099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:21.5795727Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:21.5796024Z         
2025-03-14T05:02:21.5796624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:21.5797343Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:21.5797620Z         
2025-03-14T05:02:21.5798013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.5798518Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:02:21.5798788Z         
2025-03-14T05:02:21.5799318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:21.5799943Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:02:21.5800223Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:21.5800499Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:21.5800734Z         
2025-03-14T05:02:21.5801279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:21.5801958Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:21.5802408Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:02:21.5802756Z         
2025-03-14T05:02:21.5803297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:21.5803971Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:21.5804259Z         
2025-03-14T05:02:21.5804642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.5805146Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:21.5805417Z         
2025-03-14T05:02:21.5805912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:21.5806503Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:21.5806769Z         
2025-03-14T05:02:21.5807159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.5807657Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:02:21.5807926Z         
2025-03-14T05:02:21.5808388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:21.5808971Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:21.5809228Z         
2025-03-14T05:02:21.5809802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:21.5810500Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:02:21.5810806Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:21.5811155Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:21.5812415Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:21.5812675Z         
2025-03-14T05:02:21.5813133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:21.5813671Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:21.5813914Z         
2025-03-14T05:02:21.5814446Z 
2025-03-14T05:02:21.5814540Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:21.5867239Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:02:21.5918973Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:02:21.5919515Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5920322Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5921221Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5922143Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5922983Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5923725Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5924503Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5925342Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5926076Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5926798Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5927488Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5928219Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5928966Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5929683Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5930374Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5931028Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5931747Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5932555Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5933311Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5934029Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5934705Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5935411Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5936184Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5936920Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5937646Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5938313Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5938991Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5939723Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5940460Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5941163Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5941839Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5942534Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5942883Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5943220Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5943538Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5943842Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5944259Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5944631Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5944969Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5945306Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5945603Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5945993Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5946350Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5946693Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5947025Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5947325Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5947688Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5948061Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5948411Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5948801Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5949125Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5949488Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5949838Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5950193Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5950522Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5950830Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5951189Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5951555Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5951894Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5952232Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5952544Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5952917Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5953282Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5953623Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5953960Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5954262Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5954608Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5954953Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5955289Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5955617Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5955942Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5956306Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5956653Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5956999Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5957324Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5957618Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5957955Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5958298Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5958624Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5961738Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5962042Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5962388Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5962735Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5963056Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5963416Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5963723Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5964093Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5964468Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5964814Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5965128Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5965420Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5965792Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5966159Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5966499Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5966838Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5967146Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5967545Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5967941Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5968271Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5968692Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5968986Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5969376Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5969761Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5970110Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5970423Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5970715Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5971086Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5971481Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5971827Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5972131Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5972417Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5972748Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5973086Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5973413Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5973732Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5974026Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5974373Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5974721Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5975070Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5975391Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5975675Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5976023Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5976359Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5976686Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5977004Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5977301Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5977665Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5978020Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5978349Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5978659Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5978954Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5979296Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5979642Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5979969Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5980292Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5980628Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.5981002Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5981394Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5981921Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.5982293Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.5982626Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5983030Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5983400Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5983738Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5984165Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5984462Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5984835Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5985225Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5985578Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5985916Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5986225Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5986597Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5986936Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5987273Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5987603Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5987912Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5988270Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5988675Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5989021Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5989348Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5989655Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5990015Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5990377Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5990712Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5991065Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5991380Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5991759Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5992121Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5992463Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5992800Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5993102Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5993471Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5993829Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5994176Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5994505Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5994817Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5995146Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5995500Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5995823Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.5996130Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.5996415Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.5996745Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5997084Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5997416Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.5997752Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.5998053Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:02:21.5998389Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.5998724Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.5999034Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.5999344Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.5999620Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:02:21.5999960Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6000285Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6000602Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6000914Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6001189Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6001543Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6001868Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6002187Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6002494Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6002778Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6003110Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6003441Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6003770Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6004085Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6004384Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6004719Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6005053Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6005367Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6005680Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6005964Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6006307Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6006644Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6006957Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6007271Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6007549Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6007903Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6008230Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6008551Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6008862Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6009160Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6009509Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6009850Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6010180Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6010493Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6010775Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6011106Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6011437Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6011748Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6012061Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6012362Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.6012707Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6013055Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6013380Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.6013712Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.6014013Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6014361Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6014693Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6015020Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6015338Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6015621Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6015971Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6016321Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6016662Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6016985Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6017275Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6017613Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6017957Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6018284Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6018593Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6018887Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6019257Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6019631Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6019967Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6020333Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6020633Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6021010Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6021373Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6021707Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6022053Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6022362Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6022769Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6023144Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6023513Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6023847Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6024315Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:02:21.6024695Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:02:21.6025057Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:02:21.6025480Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:02:21.6025844Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:02:21.6026205Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:02:21.6026552Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:02:21.6026634Z         
2025-03-14T05:02:21.6026940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6027433Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6027531Z         
2025-03-14T05:02:21.6027830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6029281Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6029354Z         
2025-03-14T05:02:21.6029654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:02:21.6029813Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:02:21.6029890Z         
2025-03-14T05:02:21.6030270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:02:21.6030535Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:02:21.6030606Z         
2025-03-14T05:02:21.6030879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6031305Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6031383Z         
2025-03-14T05:02:21.6031671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6033210Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6033294Z         
2025-03-14T05:02:21.6033595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6033752Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:02:21.6033820Z         
2025-03-14T05:02:21.6034096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6034531Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6034599Z         
2025-03-14T05:02:21.6034874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6036400Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6036477Z         
2025-03-14T05:02:21.6036767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6036933Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:02:21.6037014Z         
2025-03-14T05:02:21.6037276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6037724Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6037790Z         
2025-03-14T05:02:21.6038066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6039579Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6039653Z         
2025-03-14T05:02:21.6039908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6040352Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6040443Z         
2025-03-14T05:02:21.6040709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6042305Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6042373Z         
2025-03-14T05:02:21.6042663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6042819Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:02:21.6042885Z         
2025-03-14T05:02:21.6043201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6043356Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:02:21.6043445Z         
2025-03-14T05:02:21.6043711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6044137Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6044205Z         
2025-03-14T05:02:21.6044477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6046032Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6046109Z         
2025-03-14T05:02:21.6046400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6046545Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:02:21.6046617Z         
2025-03-14T05:02:21.6046869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6047300Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6047395Z         
2025-03-14T05:02:21.6047664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6049210Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6049289Z         
2025-03-14T05:02:21.6049583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6049742Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:02:21.6049821Z         
2025-03-14T05:02:21.6050086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6050519Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6050602Z         
2025-03-14T05:02:21.6050876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6052383Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6052463Z         
2025-03-14T05:02:21.6052748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6052902Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:02:21.6052982Z         
2025-03-14T05:02:21.6053261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6053422Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:02:21.6053489Z         
2025-03-14T05:02:21.6053745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6054174Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6054248Z         
2025-03-14T05:02:21.6054514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6056023Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6056099Z         
2025-03-14T05:02:21.6056442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6056590Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:02:21.6056671Z         
2025-03-14T05:02:21.6056942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6057358Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6057433Z         
2025-03-14T05:02:21.6057694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6059215Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6059292Z         
2025-03-14T05:02:21.6059577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6059723Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:02:21.6059787Z         
2025-03-14T05:02:21.6060045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6060469Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6060560Z         
2025-03-14T05:02:21.6060827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6062347Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6062423Z         
2025-03-14T05:02:21.6062705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6062887Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:02:21.6062952Z         
2025-03-14T05:02:21.6063253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6063423Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:02:21.6063498Z         
2025-03-14T05:02:21.6063750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6064246Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6064320Z         
2025-03-14T05:02:21.6064599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6066156Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6066227Z         
2025-03-14T05:02:21.6066530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6066688Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:02:21.6066769Z         
2025-03-14T05:02:21.6067031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6067524Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6067596Z         
2025-03-14T05:02:21.6067897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6069415Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6069517Z         
2025-03-14T05:02:21.6069822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6069990Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:02:21.6070078Z         
2025-03-14T05:02:21.6070336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6070768Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6070843Z         
2025-03-14T05:02:21.6071108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6072636Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6072713Z         
2025-03-14T05:02:21.6072968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6073424Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6073491Z         
2025-03-14T05:02:21.6073761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6075359Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6075436Z         
2025-03-14T05:02:21.6075720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6075870Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:02:21.6075943Z         
2025-03-14T05:02:21.6076241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6076406Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:02:21.6076473Z         
2025-03-14T05:02:21.6076750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6077181Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6077258Z         
2025-03-14T05:02:21.6077524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6079051Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6079129Z         
2025-03-14T05:02:21.6079413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6079562Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:02:21.6079628Z         
2025-03-14T05:02:21.6079887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6080312Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6080407Z         
2025-03-14T05:02:21.6080670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6082375Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6082462Z         
2025-03-14T05:02:21.6082764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6082966Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:02:21.6083037Z         
2025-03-14T05:02:21.6083309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6083791Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6083892Z         
2025-03-14T05:02:21.6084171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6085722Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6085798Z         
2025-03-14T05:02:21.6086077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6086242Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:02:21.6086307Z         
2025-03-14T05:02:21.6086599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6086752Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:02:21.6086828Z         
2025-03-14T05:02:21.6087076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6087530Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6087596Z         
2025-03-14T05:02:21.6087868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6089387Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6089455Z         
2025-03-14T05:02:21.6089764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6089909Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:02:21.6089999Z         
2025-03-14T05:02:21.6090247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6090693Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6090762Z         
2025-03-14T05:02:21.6091039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6092578Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6092646Z         
2025-03-14T05:02:21.6092944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6093088Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:02:21.6093162Z         
2025-03-14T05:02:21.6093415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6093857Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6093947Z         
2025-03-14T05:02:21.6094212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6095741Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6095810Z         
2025-03-14T05:02:21.6096101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6096272Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:02:21.6096349Z         
2025-03-14T05:02:21.6096653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6096830Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:02:21.6096897Z         
2025-03-14T05:02:21.6097159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6097588Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6097655Z         
2025-03-14T05:02:21.6097927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6099458Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6099536Z         
2025-03-14T05:02:21.6099821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6099972Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:02:21.6100039Z         
2025-03-14T05:02:21.6100297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6100748Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6100814Z         
2025-03-14T05:02:21.6101085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6102631Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6102709Z         
2025-03-14T05:02:21.6103009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6103172Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:02:21.6103261Z         
2025-03-14T05:02:21.6103509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6103958Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6104030Z         
2025-03-14T05:02:21.6104420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6106059Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6106135Z         
2025-03-14T05:02:21.6106448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6106622Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:02:21.6106703Z         
2025-03-14T05:02:21.6107018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6107198Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:02:21.6107292Z         
2025-03-14T05:02:21.6107581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6108054Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6108136Z         
2025-03-14T05:02:21.6108431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6110178Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6110263Z         
2025-03-14T05:02:21.6110599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6110775Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:02:21.6110850Z         
2025-03-14T05:02:21.6111139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6111611Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6111698Z         
2025-03-14T05:02:21.6111964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6113489Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6113564Z         
2025-03-14T05:02:21.6113850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6113996Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:02:21.6114062Z         
2025-03-14T05:02:21.6114324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6114757Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6114831Z         
2025-03-14T05:02:21.6115097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6116616Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6116693Z         
2025-03-14T05:02:21.6116961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6117412Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6117494Z         
2025-03-14T05:02:21.6117761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6119301Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6119378Z         
2025-03-14T05:02:21.6119665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6119809Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:02:21.6119886Z         
2025-03-14T05:02:21.6120166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6120323Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:02:21.6120391Z         
2025-03-14T05:02:21.6120649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6121062Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6121154Z         
2025-03-14T05:02:21.6121418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6122925Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6123002Z         
2025-03-14T05:02:21.6123309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6123457Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:02:21.6123523Z         
2025-03-14T05:02:21.6123797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6124236Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6124313Z         
2025-03-14T05:02:21.6124577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6126104Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6126183Z         
2025-03-14T05:02:21.6126467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6126610Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:02:21.6126674Z         
2025-03-14T05:02:21.6126933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6127352Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6127441Z         
2025-03-14T05:02:21.6127704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6129218Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6129293Z         
2025-03-14T05:02:21.6129572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6129726Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:02:21.6129806Z         
2025-03-14T05:02:21.6130096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6130257Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:02:21.6130342Z         
2025-03-14T05:02:21.6130593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6131020Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6131088Z         
2025-03-14T05:02:21.6131362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6132892Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6132965Z         
2025-03-14T05:02:21.6133263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6133400Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:02:21.6133477Z         
2025-03-14T05:02:21.6133726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6134154Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6134236Z         
2025-03-14T05:02:21.6134506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6136011Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6136079Z         
2025-03-14T05:02:21.6136381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6136514Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:02:21.6136587Z         
2025-03-14T05:02:21.6136847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6137282Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6137350Z         
2025-03-14T05:02:21.6137621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6139138Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6139208Z         
2025-03-14T05:02:21.6139496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6139640Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:02:21.6139713Z         
2025-03-14T05:02:21.6139994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6140144Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:02:21.6140208Z         
2025-03-14T05:02:21.6140481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6140890Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6140963Z         
2025-03-14T05:02:21.6141235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6142745Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6142835Z         
2025-03-14T05:02:21.6143122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6143534Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:02:21.6143618Z         
2025-03-14T05:02:21.6143884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6144398Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6144474Z         
2025-03-14T05:02:21.6144767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6146349Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6146430Z         
2025-03-14T05:02:21.6146738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6146887Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:02:21.6146958Z         
2025-03-14T05:02:21.6147234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6147688Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6147789Z         
2025-03-14T05:02:21.6148084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6149700Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6149783Z         
2025-03-14T05:02:21.6150094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6150256Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:02:21.6150333Z         
2025-03-14T05:02:21.6150644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6150820Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:02:21.6150892Z         
2025-03-14T05:02:21.6151160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6151597Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6151676Z         
2025-03-14T05:02:21.6151955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6153528Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6153606Z         
2025-03-14T05:02:21.6153893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6154038Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:02:21.6154103Z         
2025-03-14T05:02:21.6154361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6154796Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6154869Z         
2025-03-14T05:02:21.6155133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6156642Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6156733Z         
2025-03-14T05:02:21.6157018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6157174Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:02:21.6157252Z         
2025-03-14T05:02:21.6157508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6157928Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6158001Z         
2025-03-14T05:02:21.6158265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6159810Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6159885Z         
2025-03-14T05:02:21.6160166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6160319Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:02:21.6160387Z         
2025-03-14T05:02:21.6160676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6160862Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:02:21.6160935Z         
2025-03-14T05:02:21.6161184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6161607Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6161673Z         
2025-03-14T05:02:21.6161945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6163502Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6163577Z         
2025-03-14T05:02:21.6163884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6164035Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:02:21.6164107Z         
2025-03-14T05:02:21.6164355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6164782Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6164850Z         
2025-03-14T05:02:21.6165120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6166633Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6166708Z         
2025-03-14T05:02:21.6167001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6167135Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:02:21.6167206Z         
2025-03-14T05:02:21.6167455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6167896Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6167961Z         
2025-03-14T05:02:21.6168231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6169744Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6169829Z         
2025-03-14T05:02:21.6170117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6170275Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:02:21.6170369Z         
2025-03-14T05:02:21.6170655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6170806Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:02:21.6170872Z         
2025-03-14T05:02:21.6171130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6171539Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6171613Z         
2025-03-14T05:02:21.6171879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6173416Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6173492Z         
2025-03-14T05:02:21.6173780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6173939Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:02:21.6174003Z         
2025-03-14T05:02:21.6174261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6174685Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6174759Z         
2025-03-14T05:02:21.6175027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6176563Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6176639Z         
2025-03-14T05:02:21.6176937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6177094Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:02:21.6177161Z         
2025-03-14T05:02:21.6177417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6177845Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6177921Z         
2025-03-14T05:02:21.6178185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6179702Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6179781Z         
2025-03-14T05:02:21.6180038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6180512Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6180610Z         
2025-03-14T05:02:21.6180900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6182717Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6182797Z         
2025-03-14T05:02:21.6183122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6183319Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:02:21.6183401Z         
2025-03-14T05:02:21.6183705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6183890Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:02:21.6183982Z         
2025-03-14T05:02:21.6184306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6184751Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6184829Z         
2025-03-14T05:02:21.6185107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6186722Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6186806Z         
2025-03-14T05:02:21.6187109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6187261Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:02:21.6187331Z         
2025-03-14T05:02:21.6187609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6188071Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6188150Z         
2025-03-14T05:02:21.6188435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6190011Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6190093Z         
2025-03-14T05:02:21.6190407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6190558Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:02:21.6190627Z         
2025-03-14T05:02:21.6190912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6191382Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6191460Z         
2025-03-14T05:02:21.6191744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6193344Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6193423Z         
2025-03-14T05:02:21.6193716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6193888Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:02:21.6193956Z         
2025-03-14T05:02:21.6194263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6194418Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:02:21.6194511Z         
2025-03-14T05:02:21.6194777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6195220Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6195299Z         
2025-03-14T05:02:21.6195578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6197148Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6197219Z         
2025-03-14T05:02:21.6197511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6197683Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:02:21.6197765Z         
2025-03-14T05:02:21.6198023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6198445Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6198520Z         
2025-03-14T05:02:21.6198785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6200328Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6200406Z         
2025-03-14T05:02:21.6200690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6200845Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:02:21.6200915Z         
2025-03-14T05:02:21.6201178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6201616Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6201688Z         
2025-03-14T05:02:21.6201951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6203479Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6203555Z         
2025-03-14T05:02:21.6203850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6204012Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:02:21.6204090Z         
2025-03-14T05:02:21.6204382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6204542Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:02:21.6204617Z         
2025-03-14T05:02:21.6205053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.6205220Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.6205286Z         
2025-03-14T05:02:21.6205593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6205734Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:21.6205817Z         
2025-03-14T05:02:21.6206250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.6206416Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.6206481Z         
2025-03-14T05:02:21.6206782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6206924Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:02:21.6206999Z         
2025-03-14T05:02:21.6207374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:02:21.6207566Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:02:21.6207666Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:02:21.6207815Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:02:21.6207882Z         
2025-03-14T05:02:21.6208225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:02:21.6208355Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:02:21.6208430Z         
2025-03-14T05:02:21.6208758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:02:21.6208888Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:02:21.6208955Z         
2025-03-14T05:02:21.6209353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:02:21.6209569Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:02:21.6209643Z         
2025-03-14T05:02:21.6210064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:02:21.6210215Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:02:21.6210666Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:02:21.6210807Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:02:21.6210935Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:02:21.6211002Z         
2025-03-14T05:02:21.6211316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.6211444Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T05:02:21.6211519Z         
2025-03-14T05:02:21.6211775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6212550Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:02:21.6212619Z         
2025-03-14T05:02:21.6212904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:02:21.6213097Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T05:02:21.6213171Z         
2025-03-14T05:02:21.6213552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:02:21.6214403Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:02:21.6214495Z         
2025-03-14T05:02:21.6214853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:02:21.6215676Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:02:21.6215744Z         
2025-03-14T05:02:21.6216088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:02:21.6216243Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:02:21.6216394Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:02:21.6216459Z         
2025-03-14T05:02:21.6216895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:02:21.6217073Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T05:02:21.6217273Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:02:21.6217451Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:02:21.6217522Z         
2025-03-14T05:02:21.6217921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:21.6218136Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:02:21.6218210Z         
2025-03-14T05:02:21.6218643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:21.6218804Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:02:21.6218957Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:21.6219102Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:21.6219167Z         
2025-03-14T05:02:21.6219550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:21.6219724Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:02:21.6219797Z         
2025-03-14T05:02:21.6220109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:21.6220256Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:21.6220321Z         
2025-03-14T05:02:21.6220659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:21.6220792Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:21.6220929Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.6221074Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:21.6221148Z         
2025-03-14T05:02:21.6221470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:21.6221602Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:21.6221724Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:21.6221877Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:21.6221944Z         
2025-03-14T05:02:21.6222260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:21.6222383Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.6222478Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:21.6222626Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:02:21.6222701Z         
2025-03-14T05:02:21.6223026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:21.6223198Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:21.6223291Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:21.6223430Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:02:21.6223497Z         
2025-03-14T05:02:21.6223844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:21.6224003Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:21.6224197Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:21.6224276Z         
2025-03-14T05:02:21.6224597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:21.6224752Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:21.6224878Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:21.6224947Z         
2025-03-14T05:02:21.6225264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:21.6225439Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:21.6225550Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:02:21.6225626Z         
2025-03-14T05:02:21.6225925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:21.6226124Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:21.6226243Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:02:21.6226320Z         
2025-03-14T05:02:21.6226663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:21.6226842Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:21.6226909Z         
2025-03-14T05:02:21.6227258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:21.6227400Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:21.6227473Z         
2025-03-14T05:02:21.6227847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:21.6228000Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:21.6228129Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:02:21.6228294Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:21.6228437Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:02:21.6228511Z         
2025-03-14T05:02:21.6228879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:21.6229031Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:21.6229171Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:02:21.6229350Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:21.6229489Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:02:21.6229568Z         
2025-03-14T05:02:21.6229906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:21.6230037Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:21.6230201Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:21.6230347Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:02:21.6230414Z         
2025-03-14T05:02:21.6230761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:21.6230882Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:21.6231063Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:21.6231200Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:02:21.6231276Z         
2025-03-14T05:02:21.6231594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:21.6231701Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:21.6231823Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:21.6231899Z         
2025-03-14T05:02:21.6232215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:21.6232322Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:21.6232438Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:21.6232533Z         
2025-03-14T05:02:21.6232840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:21.6232968Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:21.6233099Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:21.6233173Z         
2025-03-14T05:02:21.6233482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:21.6233609Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:21.6233746Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:21.6233812Z         
2025-03-14T05:02:21.6234173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:21.6234363Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:21.6234435Z         
2025-03-14T05:02:21.6234787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:21.6234961Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:21.6235029Z         
2025-03-14T05:02:21.6235437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:21.6235628Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:21.6235705Z         
2025-03-14T05:02:21.6236194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:21.6236342Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:21.6236409Z         
2025-03-14T05:02:21.6236724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6236871Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:02:21.6236948Z         
2025-03-14T05:02:21.6237391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:21.6237518Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:02:21.6237628Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:21.6237755Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:21.6237823Z         
2025-03-14T05:02:21.6238295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:21.6238466Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:21.6238719Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:02:21.6238783Z         
2025-03-14T05:02:21.6239251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:21.6239416Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:21.6239491Z         
2025-03-14T05:02:21.6239783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6239942Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:21.6240007Z         
2025-03-14T05:02:21.6240388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:21.6240542Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:21.6240607Z         
2025-03-14T05:02:21.6240909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.6241050Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:02:21.6241122Z         
2025-03-14T05:02:21.6241501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:21.6241659Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:21.6241738Z         
2025-03-14T05:02:21.6242208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:21.6242346Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:02:21.6242473Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:21.6242624Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:21.6242761Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:21.6242824Z         
2025-03-14T05:02:21.6243196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:21.6243314Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:21.6243387Z         
2025-03-14T05:02:21.6243396Z 
2025-03-14T05:02:21.6243504Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:21.6295377Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", 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L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", 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L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:02:21.6295923Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:02:21.6296280Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6296671Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6297061Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6297430Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6297796Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6298124Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6298569Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6299001Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6299405Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6299795Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6300143Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6300572Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6300974Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6301376Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6301766Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6302103Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6302508Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6302898Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6303314Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6303699Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6304078Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.6304604Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6305079Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6305540Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.6305945Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.6306297Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6306723Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6307121Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6307470Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6307821Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6308161Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6308562Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6308942Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6309294Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6309641Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6309959Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6310341Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6310709Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6311078Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6311421Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6311748Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6312121Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6312502Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6312855Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6313213Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6313554Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6313931Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6314327Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6314682Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6315032Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6315342Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6315721Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6316103Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6316457Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6316800Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6317118Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6317495Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6317916Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6318276Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6318650Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6318977Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6319368Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6319748Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6320108Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6320479Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6320813Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6321212Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6321586Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6321951Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6322304Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6322639Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.6323038Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6323423Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6323803Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.6324165Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.6324491Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6324894Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6325292Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6325662Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6326025Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6326351Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6326732Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6327114Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6327490Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6327886Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6328222Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6328608Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6328990Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6329348Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6329703Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6330021Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6330405Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6330783Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6331147Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6331491Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6331818Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6332228Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6332606Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6332974Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6333325Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6333657Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6334037Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6334444Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6334791Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6335148Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6335479Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6335848Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6336222Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6336583Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6336943Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6337249Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6337628Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6337993Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6338347Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6338695Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6339018Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6339407Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6339781Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6340138Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6340477Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6340794Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6341172Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6343768Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6344109Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6344537Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6344845Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6345234Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6345674Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6346029Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6346377Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6346671Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6347035Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6347389Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6347725Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6348049Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6348383Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.6348754Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6349131Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6349479Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.6349813Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.6350111Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6350473Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6350896Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6351238Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6351552Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6351846Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6352185Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6352530Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6352849Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6353166Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6353451Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6353800Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6354145Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6354464Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6354799Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6355082Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6355430Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6355766Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6356095Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6356407Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6356699Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6357072Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6357422Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6357760Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6358066Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6358357Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6358696Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6359039Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6359358Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6359676Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6359965Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6360302Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6360639Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6360974Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6361288Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6361574Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6361921Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6362255Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6362579Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6362893Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6363192Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6363552Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6363897Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6364222Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6364531Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6364821Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6365159Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6365500Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6365826Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6366131Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6366422Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6366761Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6367103Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6367440Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6367756Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6368044Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6368390Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6368736Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6369057Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6369372Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6369686Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6370033Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6370385Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6370713Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6371021Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6371313Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6371656Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6371992Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6372319Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6372628Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6372918Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6373256Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6373618Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6373934Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6374252Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6374547Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6374886Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6375231Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6375549Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6375879Z         l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6376204Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6376570Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6376906Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6377229Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6377547Z         l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6377827Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6378173Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6378510Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6378836Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6379145Z         l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6379450Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:02:21.6379798Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6380170Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6380511Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:02:21.6380835Z         l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:02:21.6381123Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6381606Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6381965Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6382319Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6382670Z         l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6382959Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6383329Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6383675Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6384018Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6384426Z         l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6384754Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6385148Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6385510Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6385835Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6386184Z         l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6386511Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:02:21.6386924Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6387311Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6387677Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:02:21.6388026Z         l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:02:21.6388352Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:02:21.6388730Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6389122Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6389507Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:02:21.6389858Z         l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:02:21.6390195Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:02:21.6390575Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:02:21.6390957Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:02:21.6391313Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:02:21.6391664Z         l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:02:21.6392052Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:02:21.6392420Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:02:21.6392732Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:02:21.6393109Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:02:21.6393472Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:02:21.6393838Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:02:21.6394186Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:02:21.6394256Z         
2025-03-14T05:02:21.6394561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6395038Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6395121Z         
2025-03-14T05:02:21.6395405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6396877Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6396968Z         
2025-03-14T05:02:21.6397257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:02:21.6397406Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:02:21.6397471Z         
2025-03-14T05:02:21.6397838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:02:21.6398078Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:02:21.6398154Z         
2025-03-14T05:02:21.6398410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6398840Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6398908Z         
2025-03-14T05:02:21.6399185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6400704Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6400806Z         
2025-03-14T05:02:21.6401108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6401251Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:02:21.6401323Z         
2025-03-14T05:02:21.6401575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6402007Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6402073Z         
2025-03-14T05:02:21.6402345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6403885Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6403966Z         
2025-03-14T05:02:21.6404261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6404404Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:02:21.6404478Z         
2025-03-14T05:02:21.6404734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6405177Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6405245Z         
2025-03-14T05:02:21.6405517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6407050Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6407131Z         
2025-03-14T05:02:21.6407391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6407823Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6407899Z         
2025-03-14T05:02:21.6408160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6410069Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6410151Z         
2025-03-14T05:02:21.6410440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6410614Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:02:21.6410682Z         
2025-03-14T05:02:21.6410976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6411139Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:02:21.6411211Z         
2025-03-14T05:02:21.6411459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6411879Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6411946Z         
2025-03-14T05:02:21.6412215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6413724Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6413808Z         
2025-03-14T05:02:21.6414104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6414249Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:02:21.6414322Z         
2025-03-14T05:02:21.6414572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6415009Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6415085Z         
2025-03-14T05:02:21.6415347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6416914Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6416994Z         
2025-03-14T05:02:21.6417290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6417436Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:02:21.6417512Z         
2025-03-14T05:02:21.6417767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6418217Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6418290Z         
2025-03-14T05:02:21.6418557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6420126Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6420195Z         
2025-03-14T05:02:21.6420493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6420672Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:02:21.6420746Z         
2025-03-14T05:02:21.6421035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6421198Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:02:21.6421267Z         
2025-03-14T05:02:21.6421531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6421966Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6422035Z         
2025-03-14T05:02:21.6422315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6423894Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6423985Z         
2025-03-14T05:02:21.6424347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6424493Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:02:21.6424567Z         
2025-03-14T05:02:21.6424829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6425265Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6425334Z         
2025-03-14T05:02:21.6425619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6427192Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6427292Z         
2025-03-14T05:02:21.6427605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6427752Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:02:21.6427831Z         
2025-03-14T05:02:21.6428099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6428567Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6428638Z         
2025-03-14T05:02:21.6428929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6430592Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6430691Z         
2025-03-14T05:02:21.6431003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6431172Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:02:21.6431250Z         
2025-03-14T05:02:21.6431552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6431717Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:02:21.6431785Z         
2025-03-14T05:02:21.6432048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6432481Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6432578Z         
2025-03-14T05:02:21.6432847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6434453Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6434544Z         
2025-03-14T05:02:21.6434836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6434991Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:02:21.6435058Z         
2025-03-14T05:02:21.6435317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6435748Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6435824Z         
2025-03-14T05:02:21.6436091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6437662Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6437755Z         
2025-03-14T05:02:21.6438060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6438220Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:02:21.6438289Z         
2025-03-14T05:02:21.6438563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6439021Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6439101Z         
2025-03-14T05:02:21.6439378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6440897Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6440989Z         
2025-03-14T05:02:21.6441240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6441696Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6441764Z         
2025-03-14T05:02:21.6442043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6443632Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6443705Z         
2025-03-14T05:02:21.6443991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6444153Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:02:21.6444227Z         
2025-03-14T05:02:21.6444507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6444665Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:02:21.6444729Z         
2025-03-14T05:02:21.6444984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6445399Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6445475Z         
2025-03-14T05:02:21.6445735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6447263Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6447338Z         
2025-03-14T05:02:21.6447636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6447781Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:02:21.6447845Z         
2025-03-14T05:02:21.6448100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6448528Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6448602Z         
2025-03-14T05:02:21.6448863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6450421Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6450512Z         
2025-03-14T05:02:21.6450796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6450948Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:02:21.6451013Z         
2025-03-14T05:02:21.6451272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6451706Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6451782Z         
2025-03-14T05:02:21.6452044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6453569Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6453647Z         
2025-03-14T05:02:21.6453924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6454112Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:02:21.6454178Z         
2025-03-14T05:02:21.6454474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6454622Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:02:21.6454696Z         
2025-03-14T05:02:21.6454948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6455372Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6455449Z         
2025-03-14T05:02:21.6455715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6457272Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6457355Z         
2025-03-14T05:02:21.6457651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6457791Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:02:21.6457866Z         
2025-03-14T05:02:21.6458115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6458546Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6458623Z         
2025-03-14T05:02:21.6458884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6460423Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6460505Z         
2025-03-14T05:02:21.6460797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6460935Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:02:21.6461008Z         
2025-03-14T05:02:21.6461253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6461687Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6461761Z         
2025-03-14T05:02:21.6462023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6463566Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6463646Z         
2025-03-14T05:02:21.6463938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6464102Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:02:21.6464225Z         
2025-03-14T05:02:21.6464526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6464680Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:02:21.6464753Z         
2025-03-14T05:02:21.6465016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6465471Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6465541Z         
2025-03-14T05:02:21.6465831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6467398Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6467494Z         
2025-03-14T05:02:21.6467789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6467929Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:02:21.6468005Z         
2025-03-14T05:02:21.6468268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6468728Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6468800Z         
2025-03-14T05:02:21.6469083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6470704Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6470797Z         
2025-03-14T05:02:21.6471106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6471255Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:02:21.6471329Z         
2025-03-14T05:02:21.6471595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6472057Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6472130Z         
2025-03-14T05:02:21.6472419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6474025Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6474121Z         
2025-03-14T05:02:21.6474424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6474587Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:02:21.6474664Z         
2025-03-14T05:02:21.6474968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6475136Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:02:21.6475206Z         
2025-03-14T05:02:21.6475476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6475926Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6476002Z         
2025-03-14T05:02:21.6476280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6477923Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6478029Z         
2025-03-14T05:02:21.6478331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6478486Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:02:21.6478565Z         
2025-03-14T05:02:21.6478824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6479244Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6479318Z         
2025-03-14T05:02:21.6479580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6481086Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6481178Z         
2025-03-14T05:02:21.6481605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6481760Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:02:21.6481827Z         
2025-03-14T05:02:21.6482087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6482508Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6482588Z         
2025-03-14T05:02:21.6482851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6484451Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6484550Z         
2025-03-14T05:02:21.6484804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6485243Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6485341Z         
2025-03-14T05:02:21.6485616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6487169Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6487237Z         
2025-03-14T05:02:21.6487527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6487668Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:02:21.6487780Z         
2025-03-14T05:02:21.6488063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6488215Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:02:21.6488281Z         
2025-03-14T05:02:21.6488536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6488953Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6489030Z         
2025-03-14T05:02:21.6489294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6490834Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6490924Z         
2025-03-14T05:02:21.6491213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6491357Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:02:21.6491421Z         
2025-03-14T05:02:21.6491676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6492100Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6492174Z         
2025-03-14T05:02:21.6492437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6493943Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6494018Z         
2025-03-14T05:02:21.6494300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6494456Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:02:21.6494522Z         
2025-03-14T05:02:21.6494781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6495196Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6495271Z         
2025-03-14T05:02:21.6495533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6497065Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6497153Z         
2025-03-14T05:02:21.6497434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6497591Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:02:21.6497657Z         
2025-03-14T05:02:21.6497946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6498088Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:02:21.6498162Z         
2025-03-14T05:02:21.6498412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6498830Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6498897Z         
2025-03-14T05:02:21.6499167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6500683Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6500766Z         
2025-03-14T05:02:21.6501056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6501190Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:02:21.6501261Z         
2025-03-14T05:02:21.6501509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6501931Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6502004Z         
2025-03-14T05:02:21.6502265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6503814Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6503895Z         
2025-03-14T05:02:21.6504236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6504374Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:02:21.6504449Z         
2025-03-14T05:02:21.6504698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6505153Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6505229Z         
2025-03-14T05:02:21.6505508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6507103Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6507173Z         
2025-03-14T05:02:21.6507477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6507655Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:02:21.6507731Z         
2025-03-14T05:02:21.6508030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6508188Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:02:21.6508255Z         
2025-03-14T05:02:21.6508528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6508972Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6509041Z         
2025-03-14T05:02:21.6509329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6510971Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6511064Z         
2025-03-14T05:02:21.6511367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6511518Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:02:21.6511595Z         
2025-03-14T05:02:21.6511867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6512314Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6512387Z         
2025-03-14T05:02:21.6512675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6514257Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6514350Z         
2025-03-14T05:02:21.6514661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6514800Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:02:21.6514877Z         
2025-03-14T05:02:21.6515142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6515593Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6515662Z         
2025-03-14T05:02:21.6515946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6517572Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6517660Z         
2025-03-14T05:02:21.6517946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6518088Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:02:21.6518159Z         
2025-03-14T05:02:21.6518438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6518584Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:02:21.6518647Z         
2025-03-14T05:02:21.6518901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6519306Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6519378Z         
2025-03-14T05:02:21.6519638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6521132Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6521218Z         
2025-03-14T05:02:21.6521495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6521636Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:02:21.6521700Z         
2025-03-14T05:02:21.6521953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6522356Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6522428Z         
2025-03-14T05:02:21.6522685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6524178Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6524267Z         
2025-03-14T05:02:21.6524543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6524681Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:02:21.6524744Z         
2025-03-14T05:02:21.6524995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6525401Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6525475Z         
2025-03-14T05:02:21.6525731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6527191Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6527279Z         
2025-03-14T05:02:21.6527551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6527701Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:02:21.6527764Z         
2025-03-14T05:02:21.6528052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6528192Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:02:21.6528264Z         
2025-03-14T05:02:21.6528516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6528931Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6528998Z         
2025-03-14T05:02:21.6529269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6530803Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6530883Z         
2025-03-14T05:02:21.6531177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6531314Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:02:21.6531388Z         
2025-03-14T05:02:21.6531638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6532064Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6532130Z         
2025-03-14T05:02:21.6532400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6533912Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6533996Z         
2025-03-14T05:02:21.6534295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6534426Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:02:21.6534498Z         
2025-03-14T05:02:21.6534743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6535162Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6535230Z         
2025-03-14T05:02:21.6535502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6537045Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6537128Z         
2025-03-14T05:02:21.6537415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6537563Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:02:21.6537643Z         
2025-03-14T05:02:21.6537931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6538082Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:02:21.6538152Z         
2025-03-14T05:02:21.6538411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6538823Z         x_88: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6538902Z         
2025-03-14T05:02:21.6539168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6540676Z         x_89: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6540765Z         
2025-03-14T05:02:21.6541050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6541193Z         out_52: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:02:21.6541259Z         
2025-03-14T05:02:21.6541517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6541936Z         x_90: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_52 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6542010Z         
2025-03-14T05:02:21.6542274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6543815Z         x_91: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6543905Z         
2025-03-14T05:02:21.6544262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6544421Z         out_53: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:02:21.6544490Z         
2025-03-14T05:02:21.6544779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6545224Z         x_92: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6545313Z         
2025-03-14T05:02:21.6545575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6547099Z         x_93: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6547196Z         
2025-03-14T05:02:21.6547448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6547889Z         x_94: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:02:21.6547956Z         
2025-03-14T05:02:21.6548228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6549807Z         x_95: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6549889Z         
2025-03-14T05:02:21.6550178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6550321Z         x_93 += x_95;  out_54: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:02:21.6550395Z         
2025-03-14T05:02:21.6550676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6550829Z         out_55: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:02:21.6550895Z         
2025-03-14T05:02:21.6551157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6551577Z         x_96: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6551647Z         
2025-03-14T05:02:21.6551918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6553442Z         x_97: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6553540Z         
2025-03-14T05:02:21.6553823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6553963Z         out_56: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:02:21.6554027Z         
2025-03-14T05:02:21.6554286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6554709Z         x_98: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_56 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6554775Z         
2025-03-14T05:02:21.6555043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6556572Z         x_99: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6556660Z         
2025-03-14T05:02:21.6556945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6557085Z         out_57: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:02:21.6557151Z         
2025-03-14T05:02:21.6557409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6557843Z         x_100: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6557908Z         
2025-03-14T05:02:21.6558174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6559682Z         x_101: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6559755Z         
2025-03-14T05:02:21.6560057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6560210Z         x_101 += out_55;  out_58: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:02:21.6560282Z         
2025-03-14T05:02:21.6560565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6560720Z         out_59: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:02:21.6560787Z         
2025-03-14T05:02:21.6561043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6561453Z         x_102: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:02:21.6561526Z         
2025-03-14T05:02:21.6561790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6563339Z         x_103: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6563429Z         
2025-03-14T05:02:21.6563717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6563868Z         out_60: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:02:21.6563934Z         
2025-03-14T05:02:21.6564193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6564613Z         x_104: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (2, 2), (2, 2), 1);  out_60 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:02:21.6564688Z         
2025-03-14T05:02:21.6564950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6566451Z         x_105: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6566541Z         
2025-03-14T05:02:21.6566827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6566974Z         out_61: "f32[4, 512, 73, 75][2803200, 5475, 75, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:02:21.6567040Z         
2025-03-14T05:02:21.6567299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6567719Z         x_106: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:02:21.6567794Z         
2025-03-14T05:02:21.6568055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:02:21.6569588Z         x_107: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:02:21.6569679Z         
2025-03-14T05:02:21.6569967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:02:21.6570129Z         x_107 += out_59;  out_62: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:02:21.6570194Z         
2025-03-14T05:02:21.6570485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:02:21.6570630Z         out_63: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:02:21.6570703Z         
2025-03-14T05:02:21.6571142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.6571306Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.6571372Z         
2025-03-14T05:02:21.6571677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6571815Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:21.6571888Z         
2025-03-14T05:02:21.6572326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:21.6572486Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:02:21.6572552Z         
2025-03-14T05:02:21.6572854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6573008Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:02:21.6573083Z         
2025-03-14T05:02:21.6573457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:02:21.6573647Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:02:21.6573749Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:02:21.6573881Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:02:21.6573947Z         
2025-03-14T05:02:21.6574287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:02:21.6574417Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:02:21.6574489Z         
2025-03-14T05:02:21.6574812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:02:21.6574941Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:02:21.6575007Z         
2025-03-14T05:02:21.6575423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:02:21.6575639Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:02:21.6575738Z         
2025-03-14T05:02:21.6576162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:02:21.6576292Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:02:21.6576725Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:02:21.6576852Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:02:21.6576979Z         x_108: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:02:21.6577044Z         
2025-03-14T05:02:21.6577351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.6577478Z         tensor: "f32[82125, 4][4, 1]cpu" = x_108.to(torch.float32);  x_108 = None
2025-03-14T05:02:21.6577553Z         
2025-03-14T05:02:21.6577805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:21.6578577Z         x_109: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_63 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:02:21.6578643Z         
2025-03-14T05:02:21.6578925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:02:21.6579122Z         x_110: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_109, inplace = False);  x_109 = None
2025-03-14T05:02:21.6579210Z         
2025-03-14T05:02:21.6579594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:02:21.6580445Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:02:21.6580520Z         
2025-03-14T05:02:21.6580875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:02:21.6581872Z         x_111: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_110, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_110 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:02:21.6581965Z         
2025-03-14T05:02:21.6582319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:02:21.6582499Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:02:21.6582652Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:02:21.6582718Z         
2025-03-14T05:02:21.6583142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:02:21.6583306Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_111.view(4, -1, 4, 73, 75);  x_111 = None
2025-03-14T05:02:21.6583493Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:02:21.6583680Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:02:21.6583747Z         
2025-03-14T05:02:21.6584211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:21.6584456Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:02:21.6584537Z         
2025-03-14T05:02:21.6585013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:21.6585182Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:02:21.6585350Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:21.6585500Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:21.6585566Z         
2025-03-14T05:02:21.6585948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:21.6586182Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:02:21.6586256Z         
2025-03-14T05:02:21.6586570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:21.6586718Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:21.6586786Z         
2025-03-14T05:02:21.6587105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:21.6587251Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:21.6587398Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.6587563Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:21.6587646Z         
2025-03-14T05:02:21.6588000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:21.6588148Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:21.6588300Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:21.6588490Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:21.6588564Z         
2025-03-14T05:02:21.6588919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:21.6589072Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:21.6589184Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:21.6589321Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:02:21.6589402Z         
2025-03-14T05:02:21.6589746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:21.6589914Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:21.6590018Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:21.6590180Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:02:21.6590251Z         
2025-03-14T05:02:21.6590617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:21.6590789Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:21.6590923Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:21.6590995Z         
2025-03-14T05:02:21.6591328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:21.6591503Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:21.6591628Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:21.6591706Z         
2025-03-14T05:02:21.6592030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:21.6592207Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:21.6592348Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:02:21.6592424Z         
2025-03-14T05:02:21.6592748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:21.6592955Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:21.6593076Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:02:21.6593153Z         
2025-03-14T05:02:21.6593524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:21.6593686Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:21.6593755Z         
2025-03-14T05:02:21.6594124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:21.6594273Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:21.6594352Z         
2025-03-14T05:02:21.6594720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:21.6594880Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:21.6595020Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:02:21.6595180Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:21.6595333Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:02:21.6595410Z         
2025-03-14T05:02:21.6595758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:21.6595903Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:21.6596024Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:02:21.6596182Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:21.6596320Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:02:21.6596395Z         
2025-03-14T05:02:21.6596723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:21.6596851Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:21.6597011Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:21.6597150Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:02:21.6597214Z         
2025-03-14T05:02:21.6597548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:21.6597666Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:21.6597841Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:21.6597982Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:02:21.6598048Z         
2025-03-14T05:02:21.6598366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:21.6598483Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:21.6598610Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:21.6598676Z         
2025-03-14T05:02:21.6598995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:21.6599093Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:21.6599218Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:21.6599284Z         
2025-03-14T05:02:21.6599598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:21.6599717Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:21.6599854Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:21.6599918Z         
2025-03-14T05:02:21.6600227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:21.6600339Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:21.6600488Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:21.6600553Z         
2025-03-14T05:02:21.6600928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:21.6601125Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:21.6601199Z         
2025-03-14T05:02:21.6601532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:21.6601702Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:21.6601768Z         
2025-03-14T05:02:21.6602158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:21.6602333Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:21.6602408Z         
2025-03-14T05:02:21.6602884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:21.6603031Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:21.6603097Z         
2025-03-14T05:02:21.6603402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6603544Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:02:21.6603617Z         
2025-03-14T05:02:21.6604049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:21.6604173Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:02:21.6604279Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:21.6604404Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:21.6604486Z         
2025-03-14T05:02:21.6604947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:21.6605110Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:21.6605355Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:02:21.6605420Z         
2025-03-14T05:02:21.6605881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:21.6606056Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:21.6606126Z         
2025-03-14T05:02:21.6606429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:21.6606579Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:21.6606653Z         
2025-03-14T05:02:21.6607047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:21.6607217Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:21.6607285Z         
2025-03-14T05:02:21.6607592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:21.6607753Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:02:21.6607828Z         
2025-03-14T05:02:21.6608203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:21.6608349Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:21.6608415Z         
2025-03-14T05:02:21.6608905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:21.6609044Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:02:21.6609175Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:21.6609333Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:21.6609475Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:21.6609540Z         
2025-03-14T05:02:21.6609912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:21.6610041Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:21.6610113Z         
2025-03-14T05:02:31.4403781Z 
2025-03-14T05:02:31.4406652Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:31.4410859Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:02:31.4412722Z         l_features_res5_ = L_features_res5_
2025-03-14T05:02:31.4413158Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:02:31.4413719Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:02:31.4414266Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:02:31.4414850Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:02:31.4415470Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:02:31.4416065Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:02:31.4416754Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:02:31.4417175Z         
2025-03-14T05:02:31.4417839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:31.4418712Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:02:31.4419027Z         
2025-03-14T05:02:31.4419488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4420020Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:31.4420292Z         
2025-03-14T05:02:31.4420865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:31.4421589Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:02:31.4421897Z         
2025-03-14T05:02:31.4422304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4422819Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:02:31.4423092Z         
2025-03-14T05:02:31.4423582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:02:31.4424316Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:02:31.4424685Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:02:31.4424980Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:02:31.4425227Z         
2025-03-14T05:02:31.4425706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:02:31.4426255Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:02:31.4426541Z         
2025-03-14T05:02:31.4426977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:02:31.4427504Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:02:31.4427755Z         
2025-03-14T05:02:31.4428250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:02:31.4428935Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:02:31.4429277Z         
2025-03-14T05:02:31.4429811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:02:31.4430441Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:02:31.4430949Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:02:31.4431439Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:02:31.4431757Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:02:31.4431997Z         
2025-03-14T05:02:31.4432417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:31.4432967Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:02:31.4433215Z         
2025-03-14T05:02:31.4433569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:31.4434510Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:02:31.4435239Z         
2025-03-14T05:02:31.4435616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:02:31.4436149Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:31.4436462Z         
2025-03-14T05:02:31.4436938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:02:31.4438027Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:02:31.4438790Z         
2025-03-14T05:02:31.4439256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:02:31.4440287Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:02:31.4441038Z         
2025-03-14T05:02:31.4441475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:02:31.4442033Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:02:31.4442389Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:02:31.4442661Z         
2025-03-14T05:02:31.4443171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:02:31.4443815Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:02:31.4444205Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:02:31.4444614Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:02:31.4444906Z         
2025-03-14T05:02:31.4445429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:31.4446114Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:02:31.4446442Z         
2025-03-14T05:02:31.4446964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:31.4447615Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:02:31.4447981Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:31.4448330Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:31.4448597Z         
2025-03-14T05:02:31.4449075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:31.4449688Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:02:31.4449984Z         
2025-03-14T05:02:31.4450436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:31.4450958Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:31.4451224Z         
2025-03-14T05:02:31.4451635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:31.4452151Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:31.4452470Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:31.4452805Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:31.4453083Z         
2025-03-14T05:02:31.4453499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:31.4454009Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:31.4454342Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:31.4454681Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:31.4454964Z         
2025-03-14T05:02:31.4455385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:31.4455909Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:31.4456187Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:31.4456465Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:02:31.4456723Z         
2025-03-14T05:02:31.4457144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:31.4457689Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:31.4457995Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:31.4458284Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:02:31.4458553Z         
2025-03-14T05:02:31.4459007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:31.4459568Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:31.4459938Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:31.4460191Z         
2025-03-14T05:02:31.4460606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:31.4461167Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:31.4461512Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:31.4461763Z         
2025-03-14T05:02:31.4462176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:31.4462716Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:31.4463060Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:02:31.4463308Z         
2025-03-14T05:02:31.4463798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:31.4464481Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:31.4464861Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:02:31.4465129Z         
2025-03-14T05:02:31.4465620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:31.4466237Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:31.4466512Z         
2025-03-14T05:02:31.4466970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:31.4467532Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:31.4467804Z         
2025-03-14T05:02:31.4468272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:31.4468873Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:31.4469213Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:02:31.4469568Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:31.4469937Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:02:31.4470217Z         
2025-03-14T05:02:31.4470688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:31.4471269Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:31.4471612Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:02:31.4471963Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:31.4472327Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:02:31.4472601Z         
2025-03-14T05:02:31.4473048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:31.4473602Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:31.4473967Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:31.4474336Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:02:31.4474624Z         
2025-03-14T05:02:31.4475068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:31.4475599Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:31.4475957Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:31.4476328Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:02:31.4476601Z         
2025-03-14T05:02:31.4477028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:31.4477521Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:31.4477804Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:31.4478060Z         
2025-03-14T05:02:31.4478478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:31.4478952Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:31.4479226Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:31.4479471Z         
2025-03-14T05:02:31.4479879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:31.4480370Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:31.4480676Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:31.4480939Z         
2025-03-14T05:02:31.4481344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:31.4482120Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:31.4482482Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:31.4482732Z         
2025-03-14T05:02:31.4483175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:31.4483765Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:31.4484067Z         
2025-03-14T05:02:31.4484505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:31.4485067Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:31.4485359Z         
2025-03-14T05:02:31.4485838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:31.4486451Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:31.4486748Z         
2025-03-14T05:02:31.4487326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:31.4488041Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:31.4488347Z         
2025-03-14T05:02:31.4488748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4489279Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:02:31.4489548Z         
2025-03-14T05:02:31.4490078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:31.4490690Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:02:31.4490969Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:31.4491246Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:31.4491485Z         
2025-03-14T05:02:31.4492043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:31.4492737Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:31.4493201Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:02:31.4493557Z         
2025-03-14T05:02:31.4494121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:31.4494794Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:31.4495076Z         
2025-03-14T05:02:31.4495459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4495963Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:31.4496237Z         
2025-03-14T05:02:31.4496704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:31.4497300Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:31.4497569Z         
2025-03-14T05:02:31.4497956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:31.4498459Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:02:31.4498728Z         
2025-03-14T05:02:31.4499202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:31.4499780Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:31.4500042Z         
2025-03-14T05:02:31.4500617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:31.4501290Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:02:31.4501608Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:31.4501961Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:31.4502324Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:31.4502587Z         
2025-03-14T05:02:31.4503056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:31.4503625Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:31.4503878Z         
2025-03-14T05:02:31.4503974Z 
2025-03-14T05:02:31.4504087Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:31.4506807Z     def forward(self, L_features_res5_: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[2048, 2048, 3, 3][18432, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[2048][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:02:31.4508180Z         l_features_res5_ = L_features_res5_
2025-03-14T05:02:31.4508593Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:02:31.4509135Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:02:31.4509632Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:02:31.4510177Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:02:31.4510768Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:02:31.4511342Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:02:31.4511968Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:02:31.4512340Z         
2025-03-14T05:02:31.4512889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:31.4513539Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:02:31.4513816Z         
2025-03-14T05:02:31.4514212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4514712Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:31.4514971Z         
2025-03-14T05:02:31.4515502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:02:31.4516145Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:02:31.4516420Z         
2025-03-14T05:02:31.4516808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4517325Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:02:31.4517591Z         
2025-03-14T05:02:31.4518884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:02:31.4519545Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:02:31.4519893Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:02:31.4520172Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:02:31.4520424Z         
2025-03-14T05:02:31.4520859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:02:31.4521388Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:02:31.4521646Z         
2025-03-14T05:02:31.4522075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:02:31.4522589Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:02:31.4522842Z         
2025-03-14T05:02:31.4523323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:02:31.4523984Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:02:31.4524340Z         
2025-03-14T05:02:31.4524844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:02:31.4525438Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:02:31.4525936Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:02:31.4526419Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:02:31.4526733Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:02:31.4526966Z         
2025-03-14T05:02:31.4527352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:31.4527820Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:02:31.4528056Z         
2025-03-14T05:02:31.4528402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:02:31.4529316Z         x_1: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.conv2d(l_features_res5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res5_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:02:31.4530017Z         
2025-03-14T05:02:31.4530379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:02:31.4530894Z         x_2: "f32[4, 2048, 73, 75][11212800, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:31.4531199Z         
2025-03-14T05:02:31.4531699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:02:31.4532822Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:02:31.4533594Z         
2025-03-14T05:02:31.4534047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:02:31.4535064Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:02:31.4535794Z         
2025-03-14T05:02:31.4536234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:02:31.4536795Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:02:31.4537151Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:02:31.4537424Z         
2025-03-14T05:02:31.4537941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:02:31.4538579Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:02:31.4538968Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:02:31.4539379Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:02:31.4539685Z         
2025-03-14T05:02:31.4540189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:31.4540890Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:02:31.4541224Z         
2025-03-14T05:02:31.4541759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:31.4542415Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:02:31.4542784Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:31.4543138Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:31.4543409Z         
2025-03-14T05:02:31.4543886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:31.4544593Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:02:31.4544904Z         
2025-03-14T05:02:31.4545325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:31.4545908Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:31.4546187Z         
2025-03-14T05:02:31.4546625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:31.4547162Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:31.4547485Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:31.4547823Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:31.4548107Z         
2025-03-14T05:02:31.4548543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:31.4549078Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:31.4549400Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:31.4549753Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:31.4550039Z         
2025-03-14T05:02:31.4550460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:31.4550985Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:31.4551276Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:31.4551556Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:02:31.4551815Z         
2025-03-14T05:02:31.4552235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:31.4552777Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:31.4553094Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:31.4553369Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:02:31.4553616Z         
2025-03-14T05:02:31.4554034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:31.4554575Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:31.4554902Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:31.4555138Z         
2025-03-14T05:02:31.4555529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:31.4556035Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:31.4556357Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:31.4556596Z         
2025-03-14T05:02:31.4556983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:31.4557492Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:31.4557817Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:02:31.4558056Z         
2025-03-14T05:02:31.4558447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:31.4558984Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:31.4559358Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:02:31.4559596Z         
2025-03-14T05:02:31.4560037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:31.4560588Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:31.4560854Z         
2025-03-14T05:02:31.4561272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:31.4561797Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:31.4562051Z         
2025-03-14T05:02:31.4562481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:31.4563025Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:31.4563344Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:02:31.4563678Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:31.4564029Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:02:31.4564291Z         
2025-03-14T05:02:31.4564718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:31.4565271Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:31.4565582Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:02:31.4565911Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:31.4566256Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:02:31.4566517Z         
2025-03-14T05:02:31.4566936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:31.4567472Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:31.4567825Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:31.4568174Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:02:31.4568428Z         
2025-03-14T05:02:31.4568851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:31.4569363Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:31.4569704Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:31.4570058Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:02:31.4570317Z         
2025-03-14T05:02:31.4570719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:31.4571186Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:31.4571454Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:31.4571691Z         
2025-03-14T05:02:31.4572086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:31.4572563Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:31.4572942Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:31.4573179Z         
2025-03-14T05:02:31.4573573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:31.4574077Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:31.4574380Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:31.4574637Z         
2025-03-14T05:02:31.4575037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:31.4575521Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:31.4575825Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:31.4576080Z         
2025-03-14T05:02:31.4576532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:31.4577135Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:31.4577438Z         
2025-03-14T05:02:31.4577872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:31.4578441Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:31.4578730Z         
2025-03-14T05:02:31.4579213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:31.4579840Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:31.4580140Z         
2025-03-14T05:02:31.4580727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:31.4581667Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:31.4581946Z         
2025-03-14T05:02:31.4582341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4582851Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:02:31.4583113Z         
2025-03-14T05:02:31.4583656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:31.4584349Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:02:31.4584640Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:31.4584928Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:31.4585176Z         
2025-03-14T05:02:31.4585759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:31.4586451Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:31.4586975Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:02:31.4587339Z         
2025-03-14T05:02:31.4587936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:31.4588660Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:31.4588954Z         
2025-03-14T05:02:31.4589349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:31.4589871Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:31.4590149Z         
2025-03-14T05:02:31.4590630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:31.4591224Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:31.4591496Z         
2025-03-14T05:02:31.4591894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:31.4592401Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:02:31.4592672Z         
2025-03-14T05:02:31.4593144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:31.4593722Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:31.4593991Z         
2025-03-14T05:02:31.4594577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:31.4595265Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:02:31.4595589Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:31.4595974Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:31.4596322Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:31.4596586Z         
2025-03-14T05:02:31.4597068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:31.4597627Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:31.4597865Z         
2025-03-14T05:02:33.1478046Z 
2025-03-14T05:02:33.1483830Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:33.1487044Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T05:02:33.1491767Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:02:33.1496396Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:02:33.1500775Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:02:33.1504544Z         
2025-03-14T05:02:33.1505227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:02:33.1506307Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:02:33.1506668Z         
2025-03-14T05:02:33.1507292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:02:33.1508050Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:02:33.1508450Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:02:33.1508802Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:02:33.1509068Z         
2025-03-14T05:02:33.1509545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:33.1510150Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:02:33.1510440Z         
2025-03-14T05:02:33.1510844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:33.1511359Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:02:33.1511626Z         
2025-03-14T05:02:33.1512034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:33.1512532Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:33.1512845Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:33.1513166Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:02:33.1513432Z         
2025-03-14T05:02:33.1513844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:33.1514350Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:33.1514654Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:33.1514980Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:02:33.1515300Z         
2025-03-14T05:02:33.1515694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:33.1516194Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:33.1516458Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:02:33.1516726Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:02:33.1516972Z         
2025-03-14T05:02:33.1517376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:33.1517895Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:33.1518186Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:02:33.1518471Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:02:33.1518723Z         
2025-03-14T05:02:33.1519154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:33.1519681Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:33.1520038Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:02:33.1520283Z         
2025-03-14T05:02:33.1520705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:33.1521282Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:33.1522709Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:02:33.1522948Z         
2025-03-14T05:02:33.1523335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:33.1523839Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:33.1524157Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:02:33.1524389Z         
2025-03-14T05:02:33.1524786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:33.1525322Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:33.1525671Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:02:33.1525904Z         
2025-03-14T05:02:33.1526330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:33.1526868Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:33.1527128Z         
2025-03-14T05:02:33.1527547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:33.1528075Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:33.1528330Z         
2025-03-14T05:02:33.1528758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:33.1529298Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:33.1529651Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:02:33.1529994Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:33.1530353Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:02:33.1530618Z         
2025-03-14T05:02:33.1531067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:33.1531620Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:33.1531947Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:02:33.1532284Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:33.1532642Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:02:33.1532910Z         
2025-03-14T05:02:33.1533341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:33.1533856Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:33.1534212Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:33.1534564Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:02:33.1534840Z         
2025-03-14T05:02:33.1535266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:33.1535786Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:33.1536126Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:33.1536485Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:02:33.1536741Z         
2025-03-14T05:02:33.1537143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:33.1537608Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:33.1537872Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:33.1538109Z         
2025-03-14T05:02:33.1538505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:33.1538963Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:33.1539222Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:33.1539451Z         
2025-03-14T05:02:33.1539842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:33.1540315Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:33.1540610Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:33.1540862Z         
2025-03-14T05:02:33.1541256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:33.1541726Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:33.1542019Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:33.1542268Z         
2025-03-14T05:02:33.1542760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:33.1543352Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:33.1543648Z         
2025-03-14T05:02:33.1544096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:33.1544821Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:02:33.1545122Z         
2025-03-14T05:02:33.1545629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:02:33.1546268Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:02:33.1546581Z         
2025-03-14T05:02:33.1547226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:02:33.1547954Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:02:33.1548219Z         
2025-03-14T05:02:33.1548750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:33.1549275Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:02:33.1549568Z         
2025-03-14T05:02:33.1550124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:02:33.1550830Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:02:33.1551187Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:02:33.1551478Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:02:33.1551722Z         
2025-03-14T05:02:33.1552304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:02:33.1553021Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:02:33.1553502Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T05:02:33.1553872Z         
2025-03-14T05:02:33.1554444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:02:33.1555161Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:02:33.1555460Z         
2025-03-14T05:02:33.1555866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:02:33.1556399Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:02:33.1556688Z         
2025-03-14T05:02:33.1557185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:02:33.1557791Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:02:33.1558063Z         
2025-03-14T05:02:33.1558449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:02:33.1558948Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T05:02:33.1559217Z         
2025-03-14T05:02:33.1559687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:02:33.1560258Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:02:33.1560527Z         
2025-03-14T05:02:33.1561099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:02:33.1561777Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:02:33.1562091Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:33.1562422Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:02:33.1562785Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:02:33.1563042Z         
2025-03-14T05:02:33.1563511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:02:33.1564058Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:02:33.1564297Z         
2025-03-14T05:02:40.4300537Z 
2025-03-14T05:02:40.4301492Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:40.4303905Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:40.4306525Z         l_stack0_ = L_stack0_
2025-03-14T05:02:40.4306931Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:02:40.4307475Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:02:40.4308030Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:02:40.4308582Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:02:40.4309125Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:02:40.4309724Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:02:40.4310647Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:02:40.4311240Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:02:40.4311750Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4312182Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4312603Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4313025Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4313338Z         
2025-03-14T05:02:40.4313776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:02:40.4314290Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:02:40.4315092Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:02:40.4315896Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:40.4316655Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:02:40.4317467Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:02:40.4317768Z         
2025-03-14T05:02:40.4318208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:02:40.4319164Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:02:40.4319871Z         
2025-03-14T05:02:40.4320288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:02:40.4321291Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:02:40.4322117Z         
2025-03-14T05:02:40.4322512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4322981Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4323241Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:40.4323481Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:40.4323764Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4324032Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:40.4324310Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:40.4324534Z         
2025-03-14T05:02:40.4324912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:40.4325845Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4326548Z         
2025-03-14T05:02:40.4327013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:40.4327593Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:02:40.4327874Z         
2025-03-14T05:02:40.4328270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:40.4328792Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:40.4329070Z         
2025-03-14T05:02:40.4329493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:40.4330011Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:40.4330321Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4330644Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:40.4330934Z         
2025-03-14T05:02:40.4331339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:40.4331833Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:40.4332127Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:40.4332443Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:40.4332708Z         
2025-03-14T05:02:40.4333111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:40.4333598Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4333858Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:40.4334120Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:40.4334358Z         
2025-03-14T05:02:40.4334765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:40.4335275Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:40.4335560Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:40.4335823Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:40.4336068Z         
2025-03-14T05:02:40.4336476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:40.4336996Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:40.4337314Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:40.4337550Z         
2025-03-14T05:02:40.4337957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:40.4338462Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:40.4338792Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:40.4339030Z         
2025-03-14T05:02:40.4339427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:40.4339941Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:40.4340273Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:40.4340520Z         
2025-03-14T05:02:40.4340921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:40.4341471Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:40.4341824Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:40.4342065Z         
2025-03-14T05:02:40.4342497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:40.4343057Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:40.4343334Z         
2025-03-14T05:02:40.4343755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:40.4344393Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:40.4344672Z         
2025-03-14T05:02:40.4345160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:40.4345768Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:40.4346122Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:40.4346474Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:40.4346848Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:40.4347125Z         
2025-03-14T05:02:40.4347645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:40.4348349Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:40.4348811Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:40.4349276Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:40.4349760Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:40.4350037Z         
2025-03-14T05:02:40.4350499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:40.4351023Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:40.4351360Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:40.4351718Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:40.4352014Z         
2025-03-14T05:02:40.4352454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:40.4352984Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:40.4353334Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:40.4353697Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:40.4353961Z         
2025-03-14T05:02:40.4354382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:40.4354865Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:40.4355140Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:40.4355384Z         
2025-03-14T05:02:40.4355794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:40.4356276Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:40.4356546Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:40.4356780Z         
2025-03-14T05:02:40.4357204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:40.4357731Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:40.4358038Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:40.4358324Z         
2025-03-14T05:02:40.4358733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:40.4359222Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:40.4359518Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:40.4359771Z         
2025-03-14T05:02:40.4360223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:40.4360825Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:40.4361126Z         
2025-03-14T05:02:40.4361561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:40.4362137Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:40.4362428Z         
2025-03-14T05:02:40.4362880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:40.4363497Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:40.4363865Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:40.4364157Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:40.4364467Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:40.4364791Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:40.4365055Z         
2025-03-14T05:02:40.4365440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4366017Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4366368Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:40.4366615Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:40.4366978Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4367330Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:40.4367584Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:40.4367807Z         
2025-03-14T05:02:40.4368230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:40.4368791Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:02:40.4369083Z         
2025-03-14T05:02:40.4369531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:40.4370136Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:40.4370498Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:40.4370808Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:40.4371130Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:40.4371448Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:40.4371723Z         
2025-03-14T05:02:40.4372269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:40.4372983Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:40.4373327Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:40.4373665Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:40.4374007Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:40.4374303Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:40.4374595Z         
2025-03-14T05:02:40.4375034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:40.4375560Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:40.4375793Z         
2025-03-14T05:02:40.4375945Z 
2025-03-14T05:02:40.4376039Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:40.4377921Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:40.4380014Z         l_stack0_ = L_stack0_
2025-03-14T05:02:40.4380373Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:02:40.4380873Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:02:40.4381367Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:02:40.4382174Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:02:40.4382756Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:02:40.4383391Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:02:40.4384022Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:02:40.4384825Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:02:40.4385422Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4385853Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4386326Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4386741Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4387053Z         
2025-03-14T05:02:40.4387453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:02:40.4402870Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:02:40.4403998Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:02:40.4405233Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:40.4406432Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:02:40.4407598Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:02:40.4408019Z         
2025-03-14T05:02:40.4408649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:02:40.4410256Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:02:40.4411415Z         
2025-03-14T05:02:40.4412036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:02:40.4413905Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:02:40.4415044Z         
2025-03-14T05:02:40.4415631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4416425Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4416853Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:40.4417243Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:40.4417712Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4418162Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:40.4418563Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:40.4418937Z         
2025-03-14T05:02:40.4419594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:40.4421529Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4422895Z         
2025-03-14T05:02:40.4423752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:40.4424949Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:02:40.4425435Z         
2025-03-14T05:02:40.4426189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:40.4427174Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:40.4427663Z         
2025-03-14T05:02:40.4428424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:40.4429357Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:40.4429899Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4430465Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:40.4430930Z         
2025-03-14T05:02:40.4431689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:40.4432622Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:40.4433156Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:40.4433739Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:40.4434194Z         
2025-03-14T05:02:40.4434939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:40.4435873Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4436308Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:40.4436780Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:40.4437187Z         
2025-03-14T05:02:40.4437920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:40.4438881Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:40.4439383Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:40.4439847Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:40.4440258Z         
2025-03-14T05:02:40.4441012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:40.4441967Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:40.4442565Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:40.4442980Z         
2025-03-14T05:02:40.4443700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:40.4444642Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:40.4445217Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:40.4445678Z         
2025-03-14T05:02:40.4446426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:40.4447367Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:40.4447985Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:40.4448385Z         
2025-03-14T05:02:40.4449107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:40.4450124Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:40.4450765Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:40.4451162Z         
2025-03-14T05:02:40.4451951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:40.4452946Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:40.4453391Z         
2025-03-14T05:02:40.4454164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:40.4455149Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:40.4455595Z         
2025-03-14T05:02:40.4456399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:40.4457420Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:40.4457986Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:40.4458583Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:40.4459209Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:40.4459656Z         
2025-03-14T05:02:40.4460464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:40.4461508Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:40.4462071Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:40.4462658Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:40.4463276Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:40.4463727Z         
2025-03-14T05:02:40.4464598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:40.4465580Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:40.4466168Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:40.4466776Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:40.4467199Z         
2025-03-14T05:02:40.4467989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:40.4468914Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:40.4469540Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:40.4470205Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:40.4470642Z         
2025-03-14T05:02:40.4471374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:40.4472262Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:40.4472713Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:40.4473106Z         
2025-03-14T05:02:40.4473823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:40.4474668Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:40.4475119Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:40.4475504Z         
2025-03-14T05:02:40.4476210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:40.4477072Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:40.4477574Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:40.4477986Z         
2025-03-14T05:02:40.4478695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:40.4479570Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:40.4480077Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:40.4480494Z         
2025-03-14T05:02:40.4481275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:40.4482548Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:40.4483046Z         
2025-03-14T05:02:40.4483808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:40.4484922Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:40.4485414Z         
2025-03-14T05:02:40.4486224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:40.4487337Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:40.4487972Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:40.4488478Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:40.4488991Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:40.4489538Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:40.4489981Z         
2025-03-14T05:02:40.4490655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4491668Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4492273Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:40.4492666Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:40.4493360Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4494006Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:40.4494421Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:40.4494817Z         
2025-03-14T05:02:40.4495569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:40.4496601Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:02:40.4497087Z         
2025-03-14T05:02:40.4497868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:40.4498967Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:40.4499603Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:40.4500095Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:40.4500615Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:40.4501170Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:40.4501606Z         
2025-03-14T05:02:40.4502594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:40.4503859Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:40.4504576Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:40.4505196Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:40.4505804Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:40.4506309Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:40.4506713Z         
2025-03-14T05:02:40.4507508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:40.4508515Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:40.4508905Z         
2025-03-14T05:02:40.4509163Z 
2025-03-14T05:02:40.4509313Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:40.4512885Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:40.4516732Z         l_stack0_ = L_stack0_
2025-03-14T05:02:40.4517374Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:02:40.4518273Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:02:40.4519146Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:02:40.4520047Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:02:40.4521006Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:02:40.4522059Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:02:40.4523115Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:02:40.4524156Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:02:40.4525008Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4525728Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4526426Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4527132Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4527667Z         
2025-03-14T05:02:40.4528310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:02:40.4529133Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:02:40.4530360Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:02:40.4531665Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:40.4533006Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:02:40.4534303Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:02:40.4534775Z         
2025-03-14T05:02:40.4535470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:02:40.4537240Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:02:40.4538521Z         
2025-03-14T05:02:40.4539227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:02:40.4541092Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:02:40.4542503Z         
2025-03-14T05:02:40.4543168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4544042Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4544597Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:40.4545033Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:40.4545539Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4545996Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:40.4546413Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:40.4546798Z         
2025-03-14T05:02:40.4547489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:40.4549335Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4550726Z         
2025-03-14T05:02:40.4551580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:40.4552679Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:02:40.4553162Z         
2025-03-14T05:02:40.4553898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:40.4554913Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:40.4555416Z         
2025-03-14T05:02:40.4556186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:40.4557157Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:40.4557666Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4558257Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:40.4558700Z         
2025-03-14T05:02:40.4559428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:40.4560320Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:40.4560832Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:40.4561387Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:40.4561822Z         
2025-03-14T05:02:40.4562525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:40.4563410Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4563840Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:40.4564272Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:40.4564671Z         
2025-03-14T05:02:40.4565379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:40.4566328Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:40.4566804Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:40.4567275Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:40.4567680Z         
2025-03-14T05:02:40.4568423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:40.4569336Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:40.4569898Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:40.4570285Z         
2025-03-14T05:02:40.4570975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:40.4571875Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:40.4572435Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:40.4572836Z         
2025-03-14T05:02:40.4573518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:40.4574439Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:40.4575002Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:40.4575398Z         
2025-03-14T05:02:40.4576100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:40.4577081Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:40.4577686Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:40.4578061Z         
2025-03-14T05:02:40.4578822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:40.4579786Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:40.4580215Z         
2025-03-14T05:02:40.4581006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:40.4582147Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:40.4582592Z         
2025-03-14T05:02:40.4583375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:40.4584498Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:40.4585127Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:40.4585739Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:40.4586401Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:40.4586845Z         
2025-03-14T05:02:40.4587644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:40.4588651Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:40.4589207Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:40.4589876Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:40.4590534Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:40.4590981Z         
2025-03-14T05:02:40.4591759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:40.4592719Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:40.4593297Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:40.4593903Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:40.4594325Z         
2025-03-14T05:02:40.4595093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:40.4596026Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:40.4596626Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:40.4597236Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:40.4597673Z         
2025-03-14T05:02:40.4598398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:40.4599249Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:40.4599689Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:40.4600070Z         
2025-03-14T05:02:40.4600759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:40.4601578Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:40.4602007Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:40.4602378Z         
2025-03-14T05:02:40.4603077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:40.4603926Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:40.4604529Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:40.4604927Z         
2025-03-14T05:02:40.4605611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:40.4606464Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:40.4606956Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:40.4607364Z         
2025-03-14T05:02:40.4608128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:40.4609163Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:40.4609650Z         
2025-03-14T05:02:40.4610391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:40.4611371Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:40.4611852Z         
2025-03-14T05:02:40.4612676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:40.4613733Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:40.4614267Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:40.4614627Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:40.4614938Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:40.4615264Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:40.4615535Z         
2025-03-14T05:02:40.4615928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4616500Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4616861Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:40.4617123Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:40.4617490Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4617856Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:40.4618115Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:40.4618346Z         
2025-03-14T05:02:40.4618781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:40.4619356Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:02:40.4619652Z         
2025-03-14T05:02:40.4620116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:40.4620737Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:40.4621106Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:40.4621410Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:40.4621720Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:40.4622074Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:40.4622348Z         
2025-03-14T05:02:40.4622918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:40.4623639Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:40.4623993Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:40.4624452Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:40.4624823Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:40.4625139Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:40.4625398Z         
2025-03-14T05:02:40.4625870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:40.4626407Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:40.4626650Z         
2025-03-14T05:02:40.4626738Z 
2025-03-14T05:02:40.4626837Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:40.4628834Z     def forward(self, L_stack0_: "f32[3230, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 100352][100352, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:40.4630937Z         l_stack0_ = L_stack0_
2025-03-14T05:02:40.4631284Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:02:40.4631764Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:02:40.4632236Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:02:40.4632700Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:02:40.4633211Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:02:40.4633767Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:02:40.4634323Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:02:40.4634874Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:02:40.4635357Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4635780Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4636171Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4636559Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:40.4636849Z         
2025-03-14T05:02:40.4637218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:02:40.4637690Z         x: "f32[3230, 100352][100352, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:02:40.4638403Z         x_1: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:02:40.4639108Z         x_2: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:02:40.4639821Z         x_3: "f32[3230, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:02:40.4640546Z         x_4: "f32[3230, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:02:40.4640829Z         
2025-03-14T05:02:40.4641245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:02:40.4642208Z         scores: "f32[3230, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:02:40.4642915Z         
2025-03-14T05:02:40.4643314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:02:40.4644309Z         proposal_deltas: "f32[3230, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:02:40.4645029Z         
2025-03-14T05:02:40.4645407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4645871Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4646124Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:40.4646351Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:40.4646631Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:40.4646893Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:40.4647143Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:40.4647364Z         
2025-03-14T05:02:40.4647736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:40.4648659Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4649391Z         
2025-03-14T05:02:40.4649856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:40.4650432Z         deltas: "f32[3230, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:02:40.4650707Z         
2025-03-14T05:02:40.4651107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:40.4651626Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:40.4651911Z         
2025-03-14T05:02:40.4652316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:40.4652816Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:40.4653120Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4653437Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:40.4653700Z         
2025-03-14T05:02:40.4654103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:40.4654619Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:40.4654932Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:40.4655248Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:40.4655530Z         
2025-03-14T05:02:40.4655933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:40.4656423Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:40.4656682Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:40.4656941Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:40.4657185Z         
2025-03-14T05:02:40.4657585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:40.4658101Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:40.4658388Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:40.4658653Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:40.4658898Z         
2025-03-14T05:02:40.4659308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:40.4659830Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:40.4660157Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:40.4660397Z         
2025-03-14T05:02:40.4660792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:40.4661310Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:40.4661632Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:40.4661869Z         
2025-03-14T05:02:40.4662257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:40.4662787Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:40.4663125Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:40.4663373Z         
2025-03-14T05:02:40.4663783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:40.4664463Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:40.4664841Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:40.4665096Z         
2025-03-14T05:02:40.4665591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:40.4666202Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:40.4666490Z         
2025-03-14T05:02:40.4666940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:40.4667499Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:40.4667776Z         
2025-03-14T05:02:40.4668295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:40.4668867Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:40.4669203Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:40.4669575Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:40.4669942Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:40.4670215Z         
2025-03-14T05:02:40.4670683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:40.4671263Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:40.4671601Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:40.4671946Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:40.4672308Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:40.4672581Z         
2025-03-14T05:02:40.4673033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:40.4673567Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:40.4673908Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:40.4674265Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:40.4674529Z         
2025-03-14T05:02:40.4674981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:40.4675506Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:40.4675853Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:40.4676225Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:40.4676512Z         
2025-03-14T05:02:40.4676925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:40.4677410Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:40.4677676Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:40.4677916Z         
2025-03-14T05:02:40.4678316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:40.4678781Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:40.4679042Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:40.4679279Z         
2025-03-14T05:02:40.4679676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:40.4680152Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:40.4680445Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:40.4680694Z         
2025-03-14T05:02:40.4681076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:40.4681968Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:40.4682294Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:40.4682551Z         
2025-03-14T05:02:40.4682989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:40.4683597Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:40.4683892Z         
2025-03-14T05:02:40.4684307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:40.4684862Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:40.4685152Z         
2025-03-14T05:02:40.4685598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:40.4686206Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:40.4686572Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:40.4686861Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:40.4687168Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:40.4687485Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:40.4687747Z         
2025-03-14T05:02:40.4688128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:40.4688685Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4689036Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:40.4689279Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:40.4689640Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:40.4689995Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:40.4690271Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:40.4690494Z         
2025-03-14T05:02:40.4690917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:40.4691477Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:02:40.4691768Z         
2025-03-14T05:02:40.4692215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:40.4692818Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:40.4693188Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:40.4693488Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:40.4693798Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:40.4694128Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:40.4694398Z         
2025-03-14T05:02:40.4694991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:40.4695710Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:40.4696061Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:40.4696432Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:40.4696785Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:40.4697088Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:40.4697342Z         
2025-03-14T05:02:40.4697808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:40.4698365Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:40.4698611Z         
2025-03-14T05:02:42.4551090Z 
2025-03-14T05:02:42.4557300Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:42.4562455Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:42.4563788Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:02:42.4564100Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:02:42.4564458Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4564886Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4565300Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4565707Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4566012Z         
2025-03-14T05:02:42.4566447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4566935Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4567510Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:42.4567768Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:42.4568071Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4568358Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:42.4568626Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:42.4568874Z         
2025-03-14T05:02:42.4569296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:42.4570334Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4571110Z         
2025-03-14T05:02:42.4571602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:42.4572204Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:02:42.4572495Z         
2025-03-14T05:02:42.4572976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:42.4573562Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:42.4573854Z         
2025-03-14T05:02:42.4574269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:42.4574843Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:42.4575163Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4575493Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:42.4575765Z         
2025-03-14T05:02:42.4576190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:42.4576705Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:42.4577011Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:42.4577336Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:42.4577607Z         
2025-03-14T05:02:42.4578021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:42.4578523Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4578791Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:42.4579060Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:42.4579308Z         
2025-03-14T05:02:42.4579721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:42.4580248Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:42.4580547Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:42.4580825Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:42.4581078Z         
2025-03-14T05:02:42.4581723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:42.4582309Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:42.4582660Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:42.4582913Z         
2025-03-14T05:02:42.4583337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:42.4583852Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:42.4584283Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:42.4584542Z         
2025-03-14T05:02:42.4584976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:42.4585525Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:42.4585863Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:42.4586116Z         
2025-03-14T05:02:42.4586516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:42.4587077Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:42.4587486Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:42.4587797Z         
2025-03-14T05:02:42.4588278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:42.4588899Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:42.4589186Z         
2025-03-14T05:02:42.4589648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:42.4590228Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:42.4590512Z         
2025-03-14T05:02:42.4590983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:42.4591588Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:42.4591931Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:42.4592301Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:42.4592687Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:42.4592979Z         
2025-03-14T05:02:42.4593462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:42.4594069Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:42.4594389Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:42.4594724Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:42.4595072Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:42.4595338Z         
2025-03-14T05:02:42.4595764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:42.4596307Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:42.4596637Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:42.4596985Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:42.4597241Z         
2025-03-14T05:02:42.4597682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:42.4598203Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:42.4598536Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:42.4598897Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:42.4599155Z         
2025-03-14T05:02:42.4599570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:42.4600047Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:42.4600322Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:42.4600559Z         
2025-03-14T05:02:42.4600979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:42.4601436Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:42.4601710Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:42.4601946Z         
2025-03-14T05:02:42.4602359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:42.4602834Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:42.4603127Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:42.4603379Z         
2025-03-14T05:02:42.4603771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:42.4604242Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:42.4604531Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:42.4604782Z         
2025-03-14T05:02:42.4605228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:42.4605828Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:42.4606131Z         
2025-03-14T05:02:42.4606569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:42.4607139Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:42.4607432Z         
2025-03-14T05:02:42.4607889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:42.4608531Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:42.4608961Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:42.4609264Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:42.4609595Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:42.4609927Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:42.4610202Z         
2025-03-14T05:02:42.4610603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4611172Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4611533Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:42.4611787Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:42.4612161Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4612526Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:42.4612781Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:42.4613012Z         
2025-03-14T05:02:42.4613444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:42.4614058Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:02:42.4614394Z         
2025-03-14T05:02:42.4614889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:42.4615528Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:42.4615915Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:42.4616214Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:42.4616523Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:42.4616841Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:42.4617108Z         
2025-03-14T05:02:42.4617678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:42.4618390Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:42.4618748Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:42.4619094Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:42.4619447Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:42.4619753Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:42.4620005Z         
2025-03-14T05:02:42.4620462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:42.4621003Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:42.4621248Z         
2025-03-14T05:02:42.4621347Z 
2025-03-14T05:02:42.4621444Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:42.4622282Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:42.4623100Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:02:42.4623338Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:02:42.4623667Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4624097Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4624661Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4625106Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4625434Z         
2025-03-14T05:02:42.4625852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4626347Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4626618Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:42.4626874Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:42.4627154Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4627424Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:42.4627681Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:42.4627918Z         
2025-03-14T05:02:42.4628342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:42.4629348Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4630147Z         
2025-03-14T05:02:42.4630633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:42.4631242Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:02:42.4631533Z         
2025-03-14T05:02:42.4631946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:42.4632501Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:42.4632798Z         
2025-03-14T05:02:42.4633222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:42.4633744Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:42.4634067Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4634404Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:42.4634680Z         
2025-03-14T05:02:42.4635111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:42.4635632Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:42.4635947Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:42.4636280Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:42.4636558Z         
2025-03-14T05:02:42.4636978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:42.4637488Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4637779Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:42.4638048Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:42.4638290Z         
2025-03-14T05:02:42.4638686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:42.4639191Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:42.4639473Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:42.4639740Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:42.4639981Z         
2025-03-14T05:02:42.4640383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:42.4640917Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:42.4641247Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:42.4641484Z         
2025-03-14T05:02:42.4641869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:42.4642388Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:42.4642733Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:42.4642973Z         
2025-03-14T05:02:42.4643362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:42.4643877Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:42.4644197Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:42.4644429Z         
2025-03-14T05:02:42.4644815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:42.4645349Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:42.4645690Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:42.4645924Z         
2025-03-14T05:02:42.4646352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:42.4646882Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:42.4647137Z         
2025-03-14T05:02:42.4647555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:42.4648079Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:42.4648333Z         
2025-03-14T05:02:42.4648761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:42.4649305Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:42.4649618Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:42.4649952Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:42.4650298Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:42.4650574Z         
2025-03-14T05:02:42.4651009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:42.4651546Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:42.4651862Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:42.4652189Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:42.4652532Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:42.4652790Z         
2025-03-14T05:02:42.4653209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:42.4653716Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:42.4654040Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:42.4654380Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:42.4654629Z         
2025-03-14T05:02:42.4655071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:42.4655575Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:42.4655925Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:42.4656272Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:42.4656544Z         
2025-03-14T05:02:42.4656945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:42.4657413Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:42.4657678Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:42.4657916Z         
2025-03-14T05:02:42.4658423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:42.4658899Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:42.4659166Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:42.4659404Z         
2025-03-14T05:02:42.4659799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:42.4660282Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:42.4660574Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:42.4660823Z         
2025-03-14T05:02:42.4661217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:42.4661687Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:42.4661976Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:42.4662221Z         
2025-03-14T05:02:42.4662658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:42.4663239Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:42.4663574Z         
2025-03-14T05:02:42.4663993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:42.4664640Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:42.4664946Z         
2025-03-14T05:02:42.4665423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:42.4666081Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:42.4666461Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:42.4666773Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:42.4667096Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:42.4667437Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:42.4667714Z         
2025-03-14T05:02:42.4668117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4668705Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4669091Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:42.4669351Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:42.4669750Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4670137Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:42.4670398Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:42.4670629Z         
2025-03-14T05:02:42.4671070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:42.4671695Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:02:42.4672041Z         
2025-03-14T05:02:42.4672504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:42.4673129Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:42.4673506Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:42.4673814Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:42.4674126Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:42.4674456Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:42.4674728Z         
2025-03-14T05:02:42.4675305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:42.4676022Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:42.4676385Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:42.4676739Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:42.4677097Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:42.4677412Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:42.4678588Z         
2025-03-14T05:02:42.4679034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:42.4679549Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:42.4679785Z         
2025-03-14T05:02:42.4679920Z 
2025-03-14T05:02:42.4680013Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:42.4680820Z     def forward(self, L_predictions_0_: "f32[3230, 81][81, 1]cpu", L_predictions_1_: "f32[3230, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1230 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1230 - s0, 4][4, 1]cpu"):
2025-03-14T05:02:42.4681752Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:02:42.4681991Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:02:42.4682312Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4682715Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4683113Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4683576Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:02:42.4683875Z         
2025-03-14T05:02:42.4684298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4684787Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4685043Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:02:42.4685278Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:02:42.4685554Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:02:42.4685817Z         getitem_2: "Sym(1230 - s0)" = size_1[0]
2025-03-14T05:02:42.4686066Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:02:42.4686291Z         
2025-03-14T05:02:42.4686661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:02:42.4687604Z         proposal_boxes: "f32[3230, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4688304Z         
2025-03-14T05:02:42.4688762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:02:42.4689334Z         deltas: "f32[3230, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:02:42.4689608Z         
2025-03-14T05:02:42.4690004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:02:42.4690523Z         boxes: "f32[3230, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:02:42.4690806Z         
2025-03-14T05:02:42.4691210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:02:42.4691709Z         getitem_4: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:02:42.4692010Z         getitem_5: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4692350Z         widths: "f32[3230][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:02:42.4692609Z         
2025-03-14T05:02:42.4693019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:02:42.4693527Z         getitem_6: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:02:42.4693833Z         getitem_7: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:02:42.4694157Z         heights: "f32[3230][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:02:42.4694426Z         
2025-03-14T05:02:42.4694833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:02:42.4695334Z         getitem_8: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:02:42.4695601Z         mul: "f32[3230][1]cpu" = 0.5 * widths
2025-03-14T05:02:42.4695853Z         ctr_x: "f32[3230][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:02:42.4696099Z         
2025-03-14T05:02:42.4696500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:02:42.4697029Z         getitem_9: "f32[3230][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:02:42.4697339Z         mul_1: "f32[3230][1]cpu" = 0.5 * heights
2025-03-14T05:02:42.4697620Z         ctr_y: "f32[3230][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:02:42.4697867Z         
2025-03-14T05:02:42.4698267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:02:42.4698789Z         getitem_10: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:02:42.4699115Z         dx: "f32[3230, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:02:42.4699355Z         
2025-03-14T05:02:42.4699738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:02:42.4700241Z         getitem_11: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:02:42.4700566Z         dy: "f32[3230, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:02:42.4700799Z         
2025-03-14T05:02:42.4701181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:02:42.4701681Z         getitem_12: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:02:42.4702008Z         dw: "f32[3230, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:02:42.4702249Z         
2025-03-14T05:02:42.4702642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:02:42.4703188Z         getitem_13: "f32[3230, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:02:42.4703546Z         dh: "f32[3230, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:02:42.4703780Z         
2025-03-14T05:02:42.4704281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:02:42.4704834Z         dw_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:02:42.4705106Z         
2025-03-14T05:02:42.4705556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:02:42.4706120Z         dh_1: "f32[3230, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:02:42.4706384Z         
2025-03-14T05:02:42.4706829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:02:42.4707383Z         getitem_14: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:02:42.4707711Z         mul_2: "f32[3230, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:02:42.4708055Z         getitem_15: "f32[3230, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:02:42.4708411Z         pred_ctr_x: "f32[3230, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:02:42.4708679Z         
2025-03-14T05:02:42.4709128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:02:42.4709678Z         getitem_16: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:02:42.4709999Z         mul_3: "f32[3230, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:02:42.4710354Z         getitem_17: "f32[3230, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:02:42.4710720Z         pred_ctr_y: "f32[3230, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:02:42.4710987Z         
2025-03-14T05:02:42.4711421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:02:42.4711965Z         exp: "f32[3230, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:02:42.4712303Z         getitem_18: "f32[3230, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:02:42.4712651Z         pred_w: "f32[3230, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:02:42.4712908Z         
2025-03-14T05:02:42.4713341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:02:42.4713856Z         exp_1: "f32[3230, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:02:42.4714196Z         getitem_19: "f32[3230, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:02:42.4714551Z         pred_h: "f32[3230, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:02:42.4714804Z         
2025-03-14T05:02:42.4715219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:02:42.4715695Z         mul_6: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:02:42.4715967Z         x1: "f32[3230, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:02:42.4716210Z         
2025-03-14T05:02:42.4716618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:02:42.4717091Z         mul_7: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:02:42.4717360Z         y1: "f32[3230, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:02:42.4717603Z         
2025-03-14T05:02:42.4718004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:02:42.4718496Z         mul_8: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:02:42.4718814Z         x2: "f32[3230, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:02:42.4719068Z         
2025-03-14T05:02:42.4719458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:02:42.4719934Z         mul_9: "f32[3230, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:02:42.4720225Z         y2: "f32[3230, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:02:42.4720477Z         
2025-03-14T05:02:42.4720917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:02:42.4721496Z         pred_boxes: "f32[3230, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:02:42.4721794Z         
2025-03-14T05:02:42.4722220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:02:42.4722777Z         predict_boxes: "f32[3230, 320][320, 1]cpu" = pred_boxes.reshape((3230, 320));  pred_boxes = None
2025-03-14T05:02:42.4723069Z         
2025-03-14T05:02:42.4723533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:02:42.4724160Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:02:42.4724528Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:02:42.4724819Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:02:42.4725151Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:02:42.4725469Z         getitem_23: "f32[1230 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:02:42.4725735Z         
2025-03-14T05:02:42.4726114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:02:42.4726671Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4727019Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:02:42.4727275Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:02:42.4727633Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:02:42.4727982Z         getitem_26: "Sym(1230 - s0)" = size_3[0]
2025-03-14T05:02:42.4728230Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:02:42.4728453Z         
2025-03-14T05:02:42.4728863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:02:42.4729455Z         probs: "f32[3230, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:02:42.4729782Z         
2025-03-14T05:02:42.4730228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:02:42.4730823Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:02:42.4731180Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:02:42.4731478Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:02:42.4731773Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:02:42.4732098Z         getitem_31: "f32[1230 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:02:42.4732352Z         
2025-03-14T05:02:42.4732887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:42.4733572Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:02:42.4733912Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:42.4734251Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:02:42.4734592Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:42.4734888Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:42.4735130Z         
2025-03-14T05:02:42.4735677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:42.4736239Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:42.4736476Z         
2025-03-14T05:02:44.6507719Z 
2025-03-14T05:02:44.6508346Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:44.6509298Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:02:44.6509661Z         l_scores_0_ = L_scores_0_
2025-03-14T05:02:44.6509952Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:02:44.6510162Z         
2025-03-14T05:02:44.6510789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:44.6511609Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:02:44.6511955Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:44.6512294Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:02:44.6512627Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:44.6512935Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:44.6513187Z         
2025-03-14T05:02:44.6513658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:44.6514200Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:44.6514448Z         
2025-03-14T05:02:44.6514538Z 
2025-03-14T05:02:44.6514646Z class GraphModule(torch.nn.Module):
2025-03-14T05:02:44.6514959Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:02:44.6515263Z         l_scores_0_ = L_scores_0_
2025-03-14T05:02:44.6515477Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:02:44.6515672Z         
2025-03-14T05:02:44.6516234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:02:44.6516908Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:02:44.6517238Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:02:44.6517626Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:02:44.6517951Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:02:44.6518349Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:02:44.6518599Z         
2025-03-14T05:02:44.6519056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:02:44.6519588Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:02:44.6519830Z         
2025-03-14T05:03:00.5834903Z Compilation time (from dynamo_timed): 35.340866331
2025-03-14T05:03:00.5840180Z pass
2025-03-14T05:03:00.5845488Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:00.5849881Z TIMING: entire_frame_compile:35.34087 gc:0.0354 _recursive_pre_grad_passes:0.0288 async_compile.wait:8.62863 backend_compile:23.86179 _recursive_joint_graph_passes:0.46896 _recursive_post_grad_passes:0.09653 code_gen:11.68252 inductor_compile:13.21656 total_wall_time:35.34087
2025-03-14T05:03:00.5851257Z STATS: call_* op count: 611 | FakeTensorMode.__torch_dispatch__:18545 | FakeTensor.__torch_dispatch__:1849 | ProxyTorchDispatchMode.__torch_dispatch__:5936 | attempt fast:51 | slow no contiguity match:20 | fast is_contiguous:31
2025-03-14T05:03:00.5851880Z Dynamo produced 52 graphs covering 611 ops with 42 graph breaks (6 unique)
2025-03-14T05:03:06.0327194Z 
2025-03-14T05:03:13.0557562Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:03:13.0560860Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:03:13.0563048Z cpu  eval  detectron2_fasterrcnn_r_50_fpn     
2025-03-14T05:03:26.9833300Z WARNING:common:fp64 golden ref were not generated for detectron2_fasterrcnn_r_50_fpn. Setting accuracy check to cosine
2025-03-14T05:03:27.0141783Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:36.5564026Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:46.9872024Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:03:57.2142119Z 
2025-03-14T05:03:57.2142691Z class GraphModule(torch.nn.Module):
2025-03-14T05:03:57.2220748Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:03:57.2283345Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:03:57.2283843Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2284621Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2285451Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2286248Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2287037Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2287788Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2288591Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2289446Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2290273Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2291168Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2291940Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2292741Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2293595Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2294420Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2295250Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2296047Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2297143Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2298037Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2298874Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2299671Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2300458Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.2301280Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2302153Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2302999Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.2303875Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.2304851Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2305701Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2306546Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2307363Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2308159Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2308917Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2309737Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2310589Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2311426Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2312220Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2312990Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2313796Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2314643Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2315470Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2316266Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2317039Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2317841Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2318712Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2319529Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2320321Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2321082Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2321893Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2322748Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2323589Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2324400Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2325164Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2325954Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2326804Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2327625Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2328422Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2329185Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2329977Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2330809Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2331653Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2332451Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2333226Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2334020Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2334862Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2335682Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2336513Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2337281Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2338088Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2338931Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2339746Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2340538Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2341321Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.2342143Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2343014Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2343882Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.2344891Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.2345825Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2346716Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2347660Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2348516Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2349412Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2350287Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2351208Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2352104Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2352965Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2353781Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2354568Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2355387Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2356262Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2357107Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2357929Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2358718Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2359539Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2360430Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2361301Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2362111Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2362889Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2363698Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2364577Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2365432Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2366276Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2367052Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2367882Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2368722Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2369580Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2370385Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2371158Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2371951Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2372817Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2373837Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2374629Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2375392Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2376179Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2377031Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2377851Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2378677Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2379441Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2380254Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2381102Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2382106Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2382927Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2383722Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2384578Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2385445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2386282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2387095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2387951Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2388770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2389644Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2390495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2391316Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2392128Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2392965Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2393856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2394690Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2395514Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2396347Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.2397183Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2398077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2398961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.2399812Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.2400604Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2401436Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2402291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2403133Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2403943Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2404720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2405527Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2406423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2407273Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2408104Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2408892Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2409707Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2410575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2411416Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2412232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2413016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2413828Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2414688Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2415549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2416365Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2417150Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2417960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2418842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2419705Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2420543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2421340Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2422153Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2423023Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2423867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2424836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2425709Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2426609Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2427583Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2428523Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2429471Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2430347Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2431256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2432214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2433159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2434067Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2434963Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2435866Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2436788Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2437676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2438533Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2439349Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2440201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2441110Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2441993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2442851Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2443677Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2444573Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2445463Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2446303Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2447122Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2447905Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2448715Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2449617Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2450457Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2451292Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2452063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2452875Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2453742Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2454587Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2455405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2456188Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2456994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2457858Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2458712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2459520Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2460294Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2461109Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2461969Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2462825Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2463659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2464566Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2465490Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2466390Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2467326Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2468234Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2469112Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2470033Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2470999Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2471939Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2472795Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2473576Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2474386Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2475248Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2476093Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2476909Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2478560Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.2479422Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2480349Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2481200Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.2482197Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.2482985Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2483806Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2484679Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2485508Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2486302Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2487061Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2487910Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2488751Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2489570Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2490384Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2491169Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2492006Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2492883Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2493746Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2494552Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2495339Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.2496155Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2497045Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2497890Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.2498702Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.2499483Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.2500297Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2501182Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2502024Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.2502848Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.2503628Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.2504525Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.2505436Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.2506308Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.2507127Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.2507858Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:03:57.2508368Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:03:57.2508872Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:03:57.2509362Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:03:57.2509885Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:03:57.2510368Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:03:57.2510848Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:03:57.2511328Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:03:57.2511802Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:03:57.2512282Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:03:57.2512759Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:03:57.2513237Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:03:57.2513713Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:03:57.2514191Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:03:57.2514686Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:03:57.2515177Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:03:57.2515801Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:03:57.2516556Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:03:57.2517300Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:03:57.2518051Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:03:57.2518772Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:03:57.2519491Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:03:57.2520182Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:03:57.2520918Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:03:57.2521686Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:03:57.2522434Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:03:57.2523176Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:03:57.2523636Z         
2025-03-14T05:03:57.2524026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2524895Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2525532Z         
2025-03-14T05:03:57.2525892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2527906Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2529750Z         
2025-03-14T05:03:57.2530132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:03:57.2530616Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:03:57.2530877Z         
2025-03-14T05:03:57.2531338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:03:57.2531974Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:03:57.2532319Z         
2025-03-14T05:03:57.2532656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2533466Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2534059Z         
2025-03-14T05:03:57.2534429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2536488Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2538322Z         
2025-03-14T05:03:57.2538691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2539169Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:03:57.2539423Z         
2025-03-14T05:03:57.2539757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2540540Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2541136Z         
2025-03-14T05:03:57.2541486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2543582Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2545709Z         
2025-03-14T05:03:57.2546092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2546581Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:03:57.2546839Z         
2025-03-14T05:03:57.2547179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2548068Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2548716Z         
2025-03-14T05:03:57.2549074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2551193Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2553081Z         
2025-03-14T05:03:57.2553429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2554250Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.2554878Z         
2025-03-14T05:03:57.2555237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2557397Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2559536Z         
2025-03-14T05:03:57.2559911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2560406Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:03:57.2560673Z         
2025-03-14T05:03:57.2561052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2561568Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:03:57.2561841Z         
2025-03-14T05:03:57.2562197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2563002Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2563627Z         
2025-03-14T05:03:57.2563987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2566187Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2568027Z         
2025-03-14T05:03:57.2568399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2568884Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:03:57.2569152Z         
2025-03-14T05:03:57.2569489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2570280Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2570898Z         
2025-03-14T05:03:57.2571250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2573296Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2575161Z         
2025-03-14T05:03:57.2575559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2576064Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:03:57.2576331Z         
2025-03-14T05:03:57.2576669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2577512Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2578128Z         
2025-03-14T05:03:57.2578479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2580585Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2582680Z         
2025-03-14T05:03:57.2583086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2583609Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:03:57.2583907Z         
2025-03-14T05:03:57.2584363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2584949Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:03:57.2585238Z         
2025-03-14T05:03:57.2585596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2586453Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2587100Z         
2025-03-14T05:03:57.2587481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2589765Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2591785Z         
2025-03-14T05:03:57.2592181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2592696Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:03:57.2592972Z         
2025-03-14T05:03:57.2593327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2594172Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2594813Z         
2025-03-14T05:03:57.2595181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2597316Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2599212Z         
2025-03-14T05:03:57.2599591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2600074Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:03:57.2600340Z         
2025-03-14T05:03:57.2600689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2601508Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2602128Z         
2025-03-14T05:03:57.2602473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2604633Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2606532Z         
2025-03-14T05:03:57.2606899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2607389Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:03:57.2607663Z         
2025-03-14T05:03:57.2608036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2608585Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:03:57.2608862Z         
2025-03-14T05:03:57.2609197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2609993Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2610591Z         
2025-03-14T05:03:57.2610959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2613031Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2614885Z         
2025-03-14T05:03:57.2615409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2615901Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:03:57.2616166Z         
2025-03-14T05:03:57.2616503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2617290Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2617894Z         
2025-03-14T05:03:57.2618254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2620356Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2622252Z         
2025-03-14T05:03:57.2622632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2623131Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:03:57.2623401Z         
2025-03-14T05:03:57.2623740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2624644Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2625342Z         
2025-03-14T05:03:57.2625741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2627879Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2629848Z         
2025-03-14T05:03:57.2630210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2631049Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.2631668Z         
2025-03-14T05:03:57.2632019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2634222Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2636333Z         
2025-03-14T05:03:57.2636706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2637191Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:03:57.2637461Z         
2025-03-14T05:03:57.2637824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2638318Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:03:57.2638591Z         
2025-03-14T05:03:57.2638929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2639727Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2640337Z         
2025-03-14T05:03:57.2640681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2642825Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2644719Z         
2025-03-14T05:03:57.2645092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2645619Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:03:57.2645878Z         
2025-03-14T05:03:57.2646210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2647020Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2647658Z         
2025-03-14T05:03:57.2648010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2650118Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2651996Z         
2025-03-14T05:03:57.2652370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2652857Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:03:57.2653126Z         
2025-03-14T05:03:57.2653463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2654266Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2654897Z         
2025-03-14T05:03:57.2655254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2657334Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2659208Z         
2025-03-14T05:03:57.2659579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2660091Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:03:57.2660369Z         
2025-03-14T05:03:57.2660784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2661283Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:03:57.2661569Z         
2025-03-14T05:03:57.2661909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2662704Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2663302Z         
2025-03-14T05:03:57.2663655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2666030Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2667982Z         
2025-03-14T05:03:57.2668359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2668850Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:03:57.2669136Z         
2025-03-14T05:03:57.2669482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2670316Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2670935Z         
2025-03-14T05:03:57.2671294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2673419Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2675276Z         
2025-03-14T05:03:57.2675672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2676165Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:03:57.2676435Z         
2025-03-14T05:03:57.2676779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2677592Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2678803Z         
2025-03-14T05:03:57.2679190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2681321Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2683475Z         
2025-03-14T05:03:57.2683843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2684375Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:03:57.2684644Z         
2025-03-14T05:03:57.2685008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2685493Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:03:57.2685769Z         
2025-03-14T05:03:57.2686106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2686889Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2687479Z         
2025-03-14T05:03:57.2687820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2689913Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2691768Z         
2025-03-14T05:03:57.2692124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2692597Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:03:57.2692856Z         
2025-03-14T05:03:57.2693186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2693967Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2694568Z         
2025-03-14T05:03:57.2694903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2696956Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2698795Z         
2025-03-14T05:03:57.2699162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2699640Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:03:57.2699898Z         
2025-03-14T05:03:57.2700226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2700998Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2701593Z         
2025-03-14T05:03:57.2701939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2704026Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2706026Z         
2025-03-14T05:03:57.2706394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2706866Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:03:57.2707135Z         
2025-03-14T05:03:57.2707501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2707995Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:03:57.2708270Z         
2025-03-14T05:03:57.2708604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2709390Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2709980Z         
2025-03-14T05:03:57.2710326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2712399Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2714284Z         
2025-03-14T05:03:57.2714655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2715130Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:03:57.2715390Z         
2025-03-14T05:03:57.2715727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2716554Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2717166Z         
2025-03-14T05:03:57.2717517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2719593Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2721456Z         
2025-03-14T05:03:57.2721830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2722317Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:03:57.2722570Z         
2025-03-14T05:03:57.2722897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2723672Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2724259Z         
2025-03-14T05:03:57.2724599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2726665Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2728478Z         
2025-03-14T05:03:57.2728813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2729668Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.2730394Z         
2025-03-14T05:03:57.2730742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2732937Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2735043Z         
2025-03-14T05:03:57.2735414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2735893Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:03:57.2736155Z         
2025-03-14T05:03:57.2736532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2737019Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:03:57.2737291Z         
2025-03-14T05:03:57.2737626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2738390Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2739001Z         
2025-03-14T05:03:57.2739343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2741454Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2743344Z         
2025-03-14T05:03:57.2743729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2744320Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:03:57.2744603Z         
2025-03-14T05:03:57.2744960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2745795Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2746407Z         
2025-03-14T05:03:57.2746757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2748872Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2750777Z         
2025-03-14T05:03:57.2751155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2751641Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:03:57.2751902Z         
2025-03-14T05:03:57.2752243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2753047Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2753671Z         
2025-03-14T05:03:57.2754027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2756115Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2758006Z         
2025-03-14T05:03:57.2758384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2758855Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:03:57.2759132Z         
2025-03-14T05:03:57.2759493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2759963Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:03:57.2760214Z         
2025-03-14T05:03:57.2760541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2761303Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2761877Z         
2025-03-14T05:03:57.2762216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2764239Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2766049Z         
2025-03-14T05:03:57.2766458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2766922Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:03:57.2767172Z         
2025-03-14T05:03:57.2767499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2768277Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2768865Z         
2025-03-14T05:03:57.2769210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2771285Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2773114Z         
2025-03-14T05:03:57.2773478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2773940Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:03:57.2774192Z         
2025-03-14T05:03:57.2774518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2775283Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2775872Z         
2025-03-14T05:03:57.2776212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2778229Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2780064Z         
2025-03-14T05:03:57.2780424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2780898Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:03:57.2781159Z         
2025-03-14T05:03:57.2781666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2782152Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:03:57.2782421Z         
2025-03-14T05:03:57.2782759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2783549Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2784218Z         
2025-03-14T05:03:57.2784632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2786752Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2788667Z         
2025-03-14T05:03:57.2789641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2790141Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:03:57.2790409Z         
2025-03-14T05:03:57.2790760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2791543Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2792132Z         
2025-03-14T05:03:57.2792477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2794524Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2796432Z         
2025-03-14T05:03:57.2796808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2797287Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:03:57.2797547Z         
2025-03-14T05:03:57.2797885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2798684Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2799309Z         
2025-03-14T05:03:57.2800090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2802252Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2804086Z         
2025-03-14T05:03:57.2804449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2804922Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:03:57.2805184Z         
2025-03-14T05:03:57.2805544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2806013Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:03:57.2806272Z         
2025-03-14T05:03:57.2806596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2807360Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2807934Z         
2025-03-14T05:03:57.2808300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2810376Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2812212Z         
2025-03-14T05:03:57.2812571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2813036Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:03:57.2813283Z         
2025-03-14T05:03:57.2813650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2814569Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2815180Z         
2025-03-14T05:03:57.2815528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2817569Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2819386Z         
2025-03-14T05:03:57.2819748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2820211Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:03:57.2820464Z         
2025-03-14T05:03:57.2820792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2821560Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2822166Z         
2025-03-14T05:03:57.2822506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2824616Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2826487Z         
2025-03-14T05:03:57.2826874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2827376Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:03:57.2827645Z         
2025-03-14T05:03:57.2828018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2828530Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:03:57.2828798Z         
2025-03-14T05:03:57.2829136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2829932Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2830528Z         
2025-03-14T05:03:57.2830883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2832958Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2834835Z         
2025-03-14T05:03:57.2835210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2835708Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:03:57.2835967Z         
2025-03-14T05:03:57.2836300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2837096Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2837692Z         
2025-03-14T05:03:57.2838043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2840114Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2841943Z         
2025-03-14T05:03:57.2842305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2842768Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:03:57.2843020Z         
2025-03-14T05:03:57.2843350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2844126Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2844718Z         
2025-03-14T05:03:57.2845061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2847084Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2848893Z         
2025-03-14T05:03:57.2849279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2849751Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:03:57.2850013Z         
2025-03-14T05:03:57.2850376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2850851Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:03:57.2851112Z         
2025-03-14T05:03:57.2851439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2852210Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2852787Z         
2025-03-14T05:03:57.2853129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2855206Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2857023Z         
2025-03-14T05:03:57.2857388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2857853Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:03:57.2858105Z         
2025-03-14T05:03:57.2858430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2859203Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2859790Z         
2025-03-14T05:03:57.2860132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2862153Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2864045Z         
2025-03-14T05:03:57.2864501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2865011Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:03:57.2865282Z         
2025-03-14T05:03:57.2865632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2866408Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2867035Z         
2025-03-14T05:03:57.2867427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2869658Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2871656Z         
2025-03-14T05:03:57.2872015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2872855Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.2873498Z         
2025-03-14T05:03:57.2873873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2876024Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2878090Z         
2025-03-14T05:03:57.2878452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2878919Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:03:57.2879175Z         
2025-03-14T05:03:57.2879540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2880015Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:03:57.2880273Z         
2025-03-14T05:03:57.2880603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2881393Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2882151Z         
2025-03-14T05:03:57.2882544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2884575Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2886409Z         
2025-03-14T05:03:57.2886776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2887242Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:03:57.2887497Z         
2025-03-14T05:03:57.2887829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2888603Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2889186Z         
2025-03-14T05:03:57.2889531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2891577Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2893412Z         
2025-03-14T05:03:57.2893775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2894240Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:03:57.2894487Z         
2025-03-14T05:03:57.2894824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2895654Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2896259Z         
2025-03-14T05:03:57.2896616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2898681Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2900495Z         
2025-03-14T05:03:57.2900861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2901340Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:03:57.2901609Z         
2025-03-14T05:03:57.2901969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2902438Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:03:57.2902695Z         
2025-03-14T05:03:57.2903023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2903807Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.2904469Z         
2025-03-14T05:03:57.2904845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2907046Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2908915Z         
2025-03-14T05:03:57.2909314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2909819Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:03:57.2910084Z         
2025-03-14T05:03:57.2910421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2911240Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.2911843Z         
2025-03-14T05:03:57.2912196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2914302Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2916181Z         
2025-03-14T05:03:57.2916555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2917035Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:03:57.2917300Z         
2025-03-14T05:03:57.2917641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2918456Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.2919057Z         
2025-03-14T05:03:57.2919411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.2921518Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.2923352Z         
2025-03-14T05:03:57.2923726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.2924204Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:03:57.2924490Z         
2025-03-14T05:03:57.2924852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.2925323Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:03:57.2925578Z         
2025-03-14T05:03:57.2925907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2926754Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.2927417Z         
2025-03-14T05:03:57.2927745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2928568Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.2929207Z         
2025-03-14T05:03:57.2929693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.2930398Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:03:57.2930773Z         
2025-03-14T05:03:57.2931100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2931974Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.2932631Z         
2025-03-14T05:03:57.2933057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.2933650Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:03:57.2933967Z         
2025-03-14T05:03:57.2934300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2935172Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.2935869Z         
2025-03-14T05:03:57.2936405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.2937184Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:03:57.2937633Z         
2025-03-14T05:03:57.2937969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2938859Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.2939557Z         
2025-03-14T05:03:57.2939988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.2940597Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:03:57.2940929Z         
2025-03-14T05:03:57.2941264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2942150Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.2942849Z         
2025-03-14T05:03:57.2943331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.2944112Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:03:57.2944673Z         
2025-03-14T05:03:57.2945025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2945940Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.2946623Z         
2025-03-14T05:03:57.2947058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.2947664Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:03:57.2947999Z         
2025-03-14T05:03:57.2948335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.2949310Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.2950034Z         
2025-03-14T05:03:57.2950496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:03:57.2951138Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:03:57.2951470Z         
2025-03-14T05:03:57.2951992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2952610Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.2952877Z         
2025-03-14T05:03:57.2953251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2953730Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:03:57.2953984Z         
2025-03-14T05:03:57.2954486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2955102Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.2955371Z         
2025-03-14T05:03:57.2955739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2956212Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:03:57.2956472Z         
2025-03-14T05:03:57.2956922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.2957508Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:03:57.2957837Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:03:57.2958104Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:03:57.2958352Z         
2025-03-14T05:03:57.2958765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.2959271Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:03:57.2959519Z         
2025-03-14T05:03:57.2959925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.2960422Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:03:57.2960661Z         
2025-03-14T05:03:57.2961123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.2961757Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:03:57.2962082Z         
2025-03-14T05:03:57.2962576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.2963159Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:03:57.2963785Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:03:57.2964383Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:03:57.2964685Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:03:57.2964928Z         
2025-03-14T05:03:57.2965441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2966068Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.2966335Z         
2025-03-14T05:03:57.2966711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2967191Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:03:57.2967455Z         
2025-03-14T05:03:57.2967968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2968587Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.2968858Z         
2025-03-14T05:03:57.2969242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2969734Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:03:57.2969997Z         
2025-03-14T05:03:57.2970463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.2971086Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:03:57.2971445Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:03:57.2971731Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:03:57.2972000Z         
2025-03-14T05:03:57.2972418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.2972947Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:03:57.2973196Z         
2025-03-14T05:03:57.2973609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.2974099Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:03:57.2974339Z         
2025-03-14T05:03:57.2974789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.2975421Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:03:57.2975745Z         
2025-03-14T05:03:57.2976232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.2976824Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:03:57.2977428Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:03:57.2978026Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:03:57.2978329Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:03:57.2978569Z         
2025-03-14T05:03:57.2979079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2979691Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.2979958Z         
2025-03-14T05:03:57.2980337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2980816Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:03:57.2981076Z         
2025-03-14T05:03:57.2981755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2982393Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.2982665Z         
2025-03-14T05:03:57.2983047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2983535Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:03:57.2983800Z         
2025-03-14T05:03:57.2984306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.2984927Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:03:57.2985281Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:03:57.2985604Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:03:57.2985848Z         
2025-03-14T05:03:57.2986262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.2986776Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:03:57.2987017Z         
2025-03-14T05:03:57.2987431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.2987940Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:03:57.2988189Z         
2025-03-14T05:03:57.2988655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.2989302Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:03:57.2989643Z         
2025-03-14T05:03:57.2990181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.2990801Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:03:57.2991433Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:03:57.2992067Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:03:57.2992362Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:03:57.2992603Z         
2025-03-14T05:03:57.2993117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2993745Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.2994015Z         
2025-03-14T05:03:57.2994400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2994888Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:03:57.2995152Z         
2025-03-14T05:03:57.2995664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.2996290Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.2996560Z         
2025-03-14T05:03:57.2996938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.2997422Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:03:57.2997682Z         
2025-03-14T05:03:57.2998141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.2998753Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:03:57.2999125Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:03:57.2999401Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:03:57.2999650Z         
2025-03-14T05:03:57.3000066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3000577Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:03:57.3000832Z         
2025-03-14T05:03:57.3001251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3001760Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:03:57.3002012Z         
2025-03-14T05:03:57.3002483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3003129Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:03:57.3003473Z         
2025-03-14T05:03:57.3003981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3004580Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:03:57.3005167Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:03:57.3005774Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:03:57.3006078Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:03:57.3006324Z         
2025-03-14T05:03:57.3006836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3007469Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.3007738Z         
2025-03-14T05:03:57.3008117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3008602Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:03:57.3008863Z         
2025-03-14T05:03:57.3009376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3010014Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.3010293Z         
2025-03-14T05:03:57.3010682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3011164Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:03:57.3011426Z         
2025-03-14T05:03:57.3011884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3012495Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:03:57.3012865Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:03:57.3013139Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:03:57.3013384Z         
2025-03-14T05:03:57.3013819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3014358Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:03:57.3014626Z         
2025-03-14T05:03:57.3015041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3015543Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:03:57.3015791Z         
2025-03-14T05:03:57.3016249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3016900Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:03:57.3017230Z         
2025-03-14T05:03:57.3018557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3019186Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:03:57.3019791Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:03:57.3020404Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:03:57.3020701Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:03:57.3020943Z         
2025-03-14T05:03:57.3021334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.3021831Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:03:57.3022164Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:03:57.3022490Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:03:57.3022812Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:03:57.3023131Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:03:57.3023386Z         
2025-03-14T05:03:57.3023748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3024364Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:03:57.3024445Z         
2025-03-14T05:03:57.3024759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3024967Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:03:57.3025048Z         
2025-03-14T05:03:57.3025456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3026044Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3026121Z         
2025-03-14T05:03:57.3026502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3027034Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:03:57.3027103Z         
2025-03-14T05:03:57.3027365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3027874Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:03:57.3027950Z         
2025-03-14T05:03:57.3028259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3028476Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:03:57.3028559Z         
2025-03-14T05:03:57.3028952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3029487Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3029553Z         
2025-03-14T05:03:57.3029915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3030426Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:03:57.3030505Z         
2025-03-14T05:03:57.3030757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3031238Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:03:57.3031318Z         
2025-03-14T05:03:57.3031612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3031805Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:03:57.3031879Z         
2025-03-14T05:03:57.3032281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3032786Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3032861Z         
2025-03-14T05:03:57.3033221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3033733Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:03:57.3033803Z         
2025-03-14T05:03:57.3034066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3034558Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:03:57.3034637Z         
2025-03-14T05:03:57.3034934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3035143Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:03:57.3035210Z         
2025-03-14T05:03:57.3035589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3036081Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3036156Z         
2025-03-14T05:03:57.3036513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3037007Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:03:57.3037081Z         
2025-03-14T05:03:57.3037329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3038089Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:03:57.3038156Z         
2025-03-14T05:03:57.3038443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3038649Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:03:57.3038725Z         
2025-03-14T05:03:57.3039096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3039974Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:03:57.3040051Z         
2025-03-14T05:03:57.3040423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3041255Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:03:57.3041349Z         
2025-03-14T05:03:57.3041696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:03:57.3041879Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:03:57.3042033Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:03:57.3042195Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:03:57.3042350Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:03:57.3042500Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:03:57.3042648Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:03:57.3042793Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:03:57.3042936Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:03:57.3043082Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:03:57.3043226Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:03:57.3043294Z         
2025-03-14T05:03:57.3043727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:03:57.3043906Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:03:57.3044099Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:03:57.3044287Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:03:57.3044452Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:03:57.3044653Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:03:57.3044823Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:03:57.3044977Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:03:57.3045145Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:03:57.3045326Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:03:57.3045473Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:03:57.3045647Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:03:57.3045825Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:03:57.3045971Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:03:57.3046128Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:03:57.3046315Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:03:57.3046382Z         
2025-03-14T05:03:57.3046806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3047033Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:03:57.3047117Z         
2025-03-14T05:03:57.3047574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3070787Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:03:57.3071151Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:03:57.3071348Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:03:57.3071442Z         
2025-03-14T05:03:57.3071886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3072094Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:03:57.3072176Z         
2025-03-14T05:03:57.3072535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3072691Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:03:57.3072774Z         
2025-03-14T05:03:57.3073117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3073274Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3073409Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3073584Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:03:57.3073656Z         
2025-03-14T05:03:57.3074000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3074249Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3074389Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3074553Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:03:57.3074636Z         
2025-03-14T05:03:57.3074970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3075111Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3075218Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:03:57.3075366Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:03:57.3075438Z         
2025-03-14T05:03:57.3075773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3075930Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:03:57.3076034Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:03:57.3076204Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:03:57.3076282Z         
2025-03-14T05:03:57.3076655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3076831Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3076997Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:03:57.3077071Z         
2025-03-14T05:03:57.3077372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3077534Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3077648Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:03:57.3077717Z         
2025-03-14T05:03:57.3078017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3078179Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3078291Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:03:57.3078366Z         
2025-03-14T05:03:57.3078670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3078867Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:03:57.3078978Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:03:57.3079050Z         
2025-03-14T05:03:57.3079391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3079545Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:03:57.3079609Z         
2025-03-14T05:03:57.3079954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3080122Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:03:57.3080187Z         
2025-03-14T05:03:57.3080545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3080686Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:03:57.3080824Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:03:57.3080982Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:03:57.3081132Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:03:57.3081201Z         
2025-03-14T05:03:57.3081745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3081897Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:03:57.3082034Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:03:57.3082186Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:03:57.3082417Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:03:57.3082487Z         
2025-03-14T05:03:57.3082849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3082973Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:03:57.3083176Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:03:57.3083314Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:03:57.3083387Z         
2025-03-14T05:03:57.3083715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3083841Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:03:57.3084009Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:03:57.3084153Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:03:57.3084219Z         
2025-03-14T05:03:57.3084537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3084639Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:03:57.3084769Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:03:57.3084834Z         
2025-03-14T05:03:57.3085149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3085244Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:03:57.3085368Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:03:57.3085434Z         
2025-03-14T05:03:57.3085747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3085863Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:03:57.3086005Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:03:57.3086092Z         
2025-03-14T05:03:57.3086404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3086517Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:03:57.3086651Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:03:57.3086716Z         
2025-03-14T05:03:57.3087076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3087264Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:03:57.3087342Z         
2025-03-14T05:03:57.3087675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3087852Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:03:57.3087918Z         
2025-03-14T05:03:57.3088312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3088511Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:03:57.3088586Z         
2025-03-14T05:03:57.3088999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3089237Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:03:57.3089312Z         
2025-03-14T05:03:57.3089743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3089908Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:03:57.3090061Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:03:57.3090212Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:03:57.3090277Z         
2025-03-14T05:03:57.3090652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3090821Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:03:57.3090897Z         
2025-03-14T05:03:57.3091202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3091355Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:03:57.3091420Z         
2025-03-14T05:03:57.3091737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3091873Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3092006Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3092160Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:03:57.3092234Z         
2025-03-14T05:03:57.3092548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3092694Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3092816Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3092976Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:03:57.3093050Z         
2025-03-14T05:03:57.3093362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3093481Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3093582Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:03:57.3093716Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:03:57.3093789Z         
2025-03-14T05:03:57.3094097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3094262Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:03:57.3094356Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:03:57.3094508Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:03:57.3094573Z         
2025-03-14T05:03:57.3094892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3095061Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3095182Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:03:57.3095249Z         
2025-03-14T05:03:57.3095545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3095701Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3095812Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:03:57.3095882Z         
2025-03-14T05:03:57.3096171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3096324Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3096435Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:03:57.3096509Z         
2025-03-14T05:03:57.3096810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3097001Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:03:57.3097112Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:03:57.3097184Z         
2025-03-14T05:03:57.3097517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3097665Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:03:57.3097740Z         
2025-03-14T05:03:57.3098067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3098220Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:03:57.3098294Z         
2025-03-14T05:03:57.3098642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3098789Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.3098918Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:03:57.3099087Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:03:57.3099232Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:03:57.3099307Z         
2025-03-14T05:03:57.3099654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3099804Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.3099930Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:03:57.3100091Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:03:57.3100248Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:03:57.3100323Z         
2025-03-14T05:03:57.3100665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3100806Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:03:57.3100969Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:03:57.3101115Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:03:57.3101182Z         
2025-03-14T05:03:57.3101518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3101632Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:03:57.3101808Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:03:57.3101950Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:03:57.3102016Z         
2025-03-14T05:03:57.3102338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3102437Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:03:57.3102563Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:03:57.3102627Z         
2025-03-14T05:03:57.3102938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3103031Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:03:57.3103154Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:03:57.3103218Z         
2025-03-14T05:03:57.3103525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3103645Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:03:57.3103788Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:03:57.3103881Z         
2025-03-14T05:03:57.3104264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3104387Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:03:57.3104532Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:03:57.3104604Z         
2025-03-14T05:03:57.3104980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3105184Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:03:57.3105264Z         
2025-03-14T05:03:57.3105613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3105808Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:03:57.3105876Z         
2025-03-14T05:03:57.3106299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3106505Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:03:57.3106583Z         
2025-03-14T05:03:57.3106980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3107212Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:03:57.3107278Z         
2025-03-14T05:03:57.3107716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3107868Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:03:57.3108030Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:03:57.3108175Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:03:57.3108241Z         
2025-03-14T05:03:57.3108617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3108787Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:03:57.3108859Z         
2025-03-14T05:03:57.3109166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3109315Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:03:57.3109379Z         
2025-03-14T05:03:57.3109698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3109828Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3109962Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3110108Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:03:57.3110240Z         
2025-03-14T05:03:57.3110560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3110690Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3110809Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3110968Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:03:57.3111033Z         
2025-03-14T05:03:57.3111355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3111477Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3111576Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:03:57.3111708Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:03:57.3111785Z         
2025-03-14T05:03:57.3112102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3112258Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:03:57.3112374Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:03:57.3112531Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:03:57.3112597Z         
2025-03-14T05:03:57.3112914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3113086Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3113211Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:03:57.3113276Z         
2025-03-14T05:03:57.3113584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3113734Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3113864Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:03:57.3113930Z         
2025-03-14T05:03:57.3114238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3114387Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3114506Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:03:57.3114572Z         
2025-03-14T05:03:57.3114880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3115071Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:03:57.3115183Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:03:57.3115257Z         
2025-03-14T05:03:57.3115595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3115742Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:03:57.3115810Z         
2025-03-14T05:03:57.3116149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3116303Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:03:57.3116374Z         
2025-03-14T05:03:57.3116715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3116857Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.3116985Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:03:57.3117146Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:03:57.3117285Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:03:57.3117359Z         
2025-03-14T05:03:57.3117704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3117848Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.3117972Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:03:57.3118144Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:03:57.3118298Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:03:57.3118370Z         
2025-03-14T05:03:57.3118696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3118833Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:03:57.3118993Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:03:57.3119137Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:03:57.3119205Z         
2025-03-14T05:03:57.3119543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3119655Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:03:57.3119828Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:03:57.3119961Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:03:57.3120036Z         
2025-03-14T05:03:57.3120349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3120456Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:03:57.3120571Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:03:57.3120645Z         
2025-03-14T05:03:57.3120952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3121056Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:03:57.3121172Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:03:57.3121246Z         
2025-03-14T05:03:57.3121552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3121680Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:03:57.3121832Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:03:57.3121906Z         
2025-03-14T05:03:57.3122213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3122334Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:03:57.3122465Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:03:57.3122538Z         
2025-03-14T05:03:57.3122891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3123088Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:03:57.3123161Z         
2025-03-14T05:03:57.3123499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3123668Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:03:57.3123733Z         
2025-03-14T05:03:57.3124146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3124337Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:03:57.3124411Z         
2025-03-14T05:03:57.3124833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3125046Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:03:57.3125114Z         
2025-03-14T05:03:57.3125548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3125696Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:03:57.3125856Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:03:57.3125993Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:03:57.3126067Z         
2025-03-14T05:03:57.3126436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3126614Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:03:57.3126681Z         
2025-03-14T05:03:57.3127000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3127146Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:03:57.3127219Z         
2025-03-14T05:03:57.3127533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3127671Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3127799Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3127973Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:03:57.3128037Z         
2025-03-14T05:03:57.3128359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3128483Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3128614Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3128767Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:03:57.3128840Z         
2025-03-14T05:03:57.3129148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3129280Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3129375Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:03:57.3129514Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:03:57.3129579Z         
2025-03-14T05:03:57.3129891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3130061Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:03:57.3130156Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:03:57.3130310Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:03:57.3130378Z         
2025-03-14T05:03:57.3130705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3130860Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3130979Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:03:57.3131044Z         
2025-03-14T05:03:57.3131346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3131497Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3131616Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:03:57.3131682Z         
2025-03-14T05:03:57.3131986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3132133Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3132250Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:03:57.3132316Z         
2025-03-14T05:03:57.3132619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3132802Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:03:57.3132923Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:03:57.3132989Z         
2025-03-14T05:03:57.3133328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3133468Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:03:57.3133542Z         
2025-03-14T05:03:57.3133888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3134031Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:03:57.3134095Z         
2025-03-14T05:03:57.3134446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3134579Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.3134716Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:03:57.3134871Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:03:57.3135020Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:03:57.3135085Z         
2025-03-14T05:03:57.3135434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3135575Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.3135697Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:03:57.3135870Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:03:57.3136028Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:03:57.3136102Z         
2025-03-14T05:03:57.3136443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3136563Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:03:57.3136724Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:03:57.3136864Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:03:57.3136931Z         
2025-03-14T05:03:57.3137265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3137377Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:03:57.3137546Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:03:57.3137677Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:03:57.3137751Z         
2025-03-14T05:03:57.3138053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3138156Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:03:57.3138272Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:03:57.3138343Z         
2025-03-14T05:03:57.3138645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3138747Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:03:57.3138860Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:03:57.3138932Z         
2025-03-14T05:03:57.3139232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3139372Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:03:57.3139505Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:03:57.3139578Z         
2025-03-14T05:03:57.3139880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3140001Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:03:57.3140132Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:03:57.3140205Z         
2025-03-14T05:03:57.3140549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3140744Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:03:57.3140808Z         
2025-03-14T05:03:57.3141146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3141307Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:03:57.3141378Z         
2025-03-14T05:03:57.3141786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3141964Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:03:57.3142046Z         
2025-03-14T05:03:57.3142442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3142646Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:03:57.3142715Z         
2025-03-14T05:03:57.3143140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3143296Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:03:57.3143448Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:03:57.3143581Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:03:57.3143656Z         
2025-03-14T05:03:57.3144030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3144294Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:03:57.3144370Z         
2025-03-14T05:03:57.3144709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3144860Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:03:57.3144933Z         
2025-03-14T05:03:57.3145266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3145409Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3145537Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3145724Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:03:57.3145789Z         
2025-03-14T05:03:57.3146113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3146238Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3146366Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3146519Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:03:57.3146592Z         
2025-03-14T05:03:57.3146914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3147053Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3147148Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:03:57.3147290Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:03:57.3147361Z         
2025-03-14T05:03:57.3147694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3147869Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:03:57.3147994Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:03:57.3148128Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:03:57.3148221Z         
2025-03-14T05:03:57.3148544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3148715Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3148833Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:03:57.3148905Z         
2025-03-14T05:03:57.3149216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3149375Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3149491Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:03:57.3149568Z         
2025-03-14T05:03:57.3149880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3150039Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3150154Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:03:57.3150227Z         
2025-03-14T05:03:57.3150551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3150736Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:03:57.3150856Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:03:57.3150923Z         
2025-03-14T05:03:57.3151279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3151421Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:03:57.3151510Z         
2025-03-14T05:03:57.3151854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3152001Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:03:57.3152067Z         
2025-03-14T05:03:57.3152433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3152573Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.3152704Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:03:57.3152865Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:03:57.3153013Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:03:57.3153080Z         
2025-03-14T05:03:57.3153441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3153580Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.3153731Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:03:57.3153907Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:03:57.3154059Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:03:57.3154144Z         
2025-03-14T05:03:57.3154471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3154583Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:03:57.3154738Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:03:57.3154869Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:03:57.3154941Z         
2025-03-14T05:03:57.3155264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3155375Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:03:57.3155534Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:03:57.3155668Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:03:57.3155733Z         
2025-03-14T05:03:57.3156045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3156143Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:03:57.3156262Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:03:57.3156327Z         
2025-03-14T05:03:57.3156632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3156728Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:03:57.3156846Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:03:57.3156912Z         
2025-03-14T05:03:57.3157218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3157352Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:03:57.3157488Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:03:57.3157553Z         
2025-03-14T05:03:57.3157850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3157961Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:03:57.3158094Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:03:57.3158157Z         
2025-03-14T05:03:57.3158500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3158692Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:03:57.3158757Z         
2025-03-14T05:03:57.3159085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3159239Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:03:57.3159311Z         
2025-03-14T05:03:57.3159711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3159887Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:03:57.3159965Z         
2025-03-14T05:03:57.3160445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:03:57.3160579Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:03:57.3160649Z         
2025-03-14T05:03:57.3160940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3161095Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:03:57.3161159Z         
2025-03-14T05:03:57.3161590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3161702Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:03:57.3161813Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:03:57.3161925Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:03:57.3161995Z         
2025-03-14T05:03:57.3162443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3162583Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3162811Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:03:57.3162889Z         
2025-03-14T05:03:57.3163333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3163521Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3163586Z         
2025-03-14T05:03:57.3163878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3163996Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:03:57.3164069Z         
2025-03-14T05:03:57.3164486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3164606Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:03:57.3164721Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:03:57.3164836Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:03:57.3164904Z         
2025-03-14T05:03:57.3165356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3165492Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3165732Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:03:57.3165821Z         
2025-03-14T05:03:57.3166257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3166443Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3166508Z         
2025-03-14T05:03:57.3166799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3166923Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:03:57.3166994Z         
2025-03-14T05:03:57.3167412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3167531Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:03:57.3167635Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:03:57.3167757Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:03:57.3167822Z         
2025-03-14T05:03:57.3168273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3168402Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3168640Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:03:57.3168702Z         
2025-03-14T05:03:57.3169150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3169308Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3169397Z         
2025-03-14T05:03:57.3169679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3169806Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:03:57.3169870Z         
2025-03-14T05:03:57.3170287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3170399Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:03:57.3170511Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:03:57.3170623Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:03:57.3170695Z         
2025-03-14T05:03:57.3171137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3171269Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3171503Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:03:57.3171566Z         
2025-03-14T05:03:57.3172046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3172205Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3172292Z         
2025-03-14T05:03:57.3172576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3172706Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:03:57.3172769Z         
2025-03-14T05:03:57.3173188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3173299Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:03:57.3173411Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:03:57.3173524Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:03:57.3173597Z         
2025-03-14T05:03:57.3174028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3174198Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:03:57.3174423Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:03:57.3174496Z         
2025-03-14T05:03:57.3174929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3175095Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3175162Z         
2025-03-14T05:03:57.3175450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3175586Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:03:57.3175660Z         
2025-03-14T05:03:57.3175931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3176308Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:03:57.3176383Z         
2025-03-14T05:03:57.3176659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3177122Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:03:57.3177192Z         
2025-03-14T05:03:57.3177472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3177666Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:03:57.3177760Z         
2025-03-14T05:03:57.3178157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:03:57.3178316Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:03:57.3178381Z         
2025-03-14T05:03:57.3178677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.3178824Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:03:57.3178894Z         
2025-03-14T05:03:57.3179258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:03:57.3179396Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:03:57.3179460Z         
2025-03-14T05:03:57.3179934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:03:57.3180067Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:03:57.3180197Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:03:57.3180349Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:03:57.3180486Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:03:57.3181369Z         
2025-03-14T05:03:57.3181975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:03:57.3182099Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:03:57.3182175Z         
2025-03-14T05:03:57.3182727Z 
2025-03-14T05:03:57.3182824Z class GraphModule(torch.nn.Module):
2025-03-14T05:03:57.3247920Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:03:57.3248489Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:03:57.3248909Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3249353Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3249854Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3250315Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3250726Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3251146Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3251632Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3252088Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3252499Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3252878Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3253247Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3253645Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3254052Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3254429Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3254803Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3255150Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3255558Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3255957Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3256336Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3256726Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3257086Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3257505Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3257922Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3258316Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3258717Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3259078Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3259499Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3259892Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3260276Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3260650Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3260993Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3261399Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3261793Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3262173Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3262539Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3262905Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3263308Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3263699Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3264089Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3264563Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3264975Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3265461Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3265914Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3266377Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3266747Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3267095Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3267489Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3267888Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3268262Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3268636Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3268983Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3269382Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3269804Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3270184Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3270562Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3270906Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3271314Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3271733Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3272127Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3272519Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3272870Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3273275Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3273670Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3274054Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3274427Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3274766Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3275172Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3275562Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3275967Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3276332Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3276699Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3277119Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3277521Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3277929Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3278350Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3278701Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3279116Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3279511Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3279890Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3280254Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3280602Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3280996Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3281394Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3281985Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3282357Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3282744Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3283141Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3283540Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3283915Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3284290Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3284640Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3285083Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3285483Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3285894Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3286267Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3286611Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3287015Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3287417Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3287788Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3288163Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3288503Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3288960Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3289427Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3289857Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3290277Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3290649Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3291052Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3291456Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3291890Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3292318Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3292707Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3293158Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3293593Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3294015Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3294424Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3294816Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3295683Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3296159Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3297183Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3297549Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3297901Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3298304Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3298719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3299097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3299516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3299885Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3300301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3300702Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3301084Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3301465Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3301820Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3302233Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3302652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3303021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3303390Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3303769Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3304231Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3304673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3305102Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3305524Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3306306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3306768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3307194Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3307593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3307971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3308332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3308751Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3309158Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3309561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3309929Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3310277Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3310695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3311094Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3311478Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3311841Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3312188Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3312583Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3313016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3313400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3313779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3314128Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3314523Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3314916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3315286Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3315658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3316005Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3316398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3316797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3317186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3317557Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3317897Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3318300Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3318695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3319078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3319509Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3319864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3320272Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3320672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3321063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3321453Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3321796Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3322201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3322594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3322984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3323380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3323736Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3324163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3324562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3324958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3325335Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3325723Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3326158Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3326599Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3326993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3327372Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3327732Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3328155Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3328568Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3328962Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3329359Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3329716Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3330144Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3330553Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3330943Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3331341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3331687Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3332109Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3332543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3332935Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3333335Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3333695Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3334120Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3334521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3334906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3335276Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3335624Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3336024Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3336419Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3336813Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3337179Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3337529Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3337923Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3338323Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3338716Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3339095Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3339458Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3339856Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3340256Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3340627Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3340995Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3341358Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3341767Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3342181Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3342568Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3342981Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3343322Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3343727Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3344207Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3344622Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3345044Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3345464Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3345877Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3346295Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3346690Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3347066Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3347402Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3347805Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3348196Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3348575Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3348945Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3349293Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3349714Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3350113Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3350511Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3350883Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3351237Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3351674Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3352100Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3352503Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3352873Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3353235Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3353629Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3354026Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3354412Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3354774Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3355008Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:03:57.3355221Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:03:57.3355448Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:03:57.3355671Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:03:57.3355893Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:03:57.3356100Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:03:57.3356325Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:03:57.3356530Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:03:57.3356753Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:03:57.3356960Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:03:57.3357181Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:03:57.3357383Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:03:57.3357605Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:03:57.3357835Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:03:57.3358067Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:03:57.3358279Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:03:57.3358639Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:03:57.3358988Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:03:57.3359327Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:03:57.3359666Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:03:57.3360000Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:03:57.3360322Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:03:57.3360626Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:03:57.3360993Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:03:57.3361347Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:03:57.3361692Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:03:57.3362049Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:03:57.3362116Z         
2025-03-14T05:03:57.3362401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3362928Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3363005Z         
2025-03-14T05:03:57.3363280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3364997Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3365087Z         
2025-03-14T05:03:57.3365371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:03:57.3365517Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:03:57.3365581Z         
2025-03-14T05:03:57.3365946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:03:57.3366181Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:03:57.3366253Z         
2025-03-14T05:03:57.3366501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3366982Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3367048Z         
2025-03-14T05:03:57.3367318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3369061Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3369146Z         
2025-03-14T05:03:57.3369441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3369577Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:03:57.3369650Z         
2025-03-14T05:03:57.3369898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3370385Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3370456Z         
2025-03-14T05:03:57.3370731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3372488Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3372581Z         
2025-03-14T05:03:57.3372861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3373008Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:03:57.3373073Z         
2025-03-14T05:03:57.3373326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3373810Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3373882Z         
2025-03-14T05:03:57.3374141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3375881Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3375968Z         
2025-03-14T05:03:57.3376214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3376714Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.3376779Z         
2025-03-14T05:03:57.3377046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3378896Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3378979Z         
2025-03-14T05:03:57.3379261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3379406Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:03:57.3379479Z         
2025-03-14T05:03:57.3379756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3379915Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:03:57.3379981Z         
2025-03-14T05:03:57.3380236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3380727Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3380801Z         
2025-03-14T05:03:57.3381064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3383043Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3383182Z         
2025-03-14T05:03:57.3383475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3383630Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:03:57.3383696Z         
2025-03-14T05:03:57.3383975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3384633Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3384708Z         
2025-03-14T05:03:57.3385009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3386859Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3386943Z         
2025-03-14T05:03:57.3387264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3387419Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:03:57.3387498Z         
2025-03-14T05:03:57.3387768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3388333Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3388406Z         
2025-03-14T05:03:57.3388698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3390639Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3390721Z         
2025-03-14T05:03:57.3391037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3391204Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:03:57.3391283Z         
2025-03-14T05:03:57.3391617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3391814Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:03:57.3391883Z         
2025-03-14T05:03:57.3392175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3392712Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3392790Z         
2025-03-14T05:03:57.3393067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3394977Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3395058Z         
2025-03-14T05:03:57.3395372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3395540Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:03:57.3395604Z         
2025-03-14T05:03:57.3395864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3396393Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3396466Z         
2025-03-14T05:03:57.3396728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3398526Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3398605Z         
2025-03-14T05:03:57.3398904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3399066Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:03:57.3399131Z         
2025-03-14T05:03:57.3399388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3399900Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3399967Z         
2025-03-14T05:03:57.3400239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3402035Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3402112Z         
2025-03-14T05:03:57.3402398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3402553Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:03:57.3402652Z         
2025-03-14T05:03:57.3402942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3403103Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:03:57.3403169Z         
2025-03-14T05:03:57.3403430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3403932Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3404007Z         
2025-03-14T05:03:57.3404277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3406094Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3406186Z         
2025-03-14T05:03:57.3406469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3406619Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:03:57.3406682Z         
2025-03-14T05:03:57.3406939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3407429Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3407502Z         
2025-03-14T05:03:57.3407757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3409492Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3409588Z         
2025-03-14T05:03:57.3409861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3410007Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:03:57.3410070Z         
2025-03-14T05:03:57.3410323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3410797Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3410871Z         
2025-03-14T05:03:57.3411123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3412867Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3412958Z         
2025-03-14T05:03:57.3413203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3413692Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.3413766Z         
2025-03-14T05:03:57.3414023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3415814Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3415907Z         
2025-03-14T05:03:57.3416180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3416337Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:03:57.3416404Z         
2025-03-14T05:03:57.3416698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3416852Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:03:57.3416925Z         
2025-03-14T05:03:57.3417174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3417667Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3417732Z         
2025-03-14T05:03:57.3418021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3419806Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3419895Z         
2025-03-14T05:03:57.3420188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3420332Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:03:57.3420404Z         
2025-03-14T05:03:57.3420652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3421151Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3421218Z         
2025-03-14T05:03:57.3421489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3423253Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3423339Z         
2025-03-14T05:03:57.3423630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3423773Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:03:57.3423849Z         
2025-03-14T05:03:57.3424098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3424701Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3424773Z         
2025-03-14T05:03:57.3425063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3426868Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3426965Z         
2025-03-14T05:03:57.3427257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3427416Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:03:57.3427492Z         
2025-03-14T05:03:57.3427774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3427932Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:03:57.3428011Z         
2025-03-14T05:03:57.3428265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3428755Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3428839Z         
2025-03-14T05:03:57.3429112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3430887Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3430968Z         
2025-03-14T05:03:57.3431257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3431416Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:03:57.3431489Z         
2025-03-14T05:03:57.3431756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3432255Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3432336Z         
2025-03-14T05:03:57.3432609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3434392Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3434459Z         
2025-03-14T05:03:57.3434751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3434891Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:03:57.3434966Z         
2025-03-14T05:03:57.3435210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3435706Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3435790Z         
2025-03-14T05:03:57.3436059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3437848Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3437917Z         
2025-03-14T05:03:57.3438219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3438388Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:03:57.3438476Z         
2025-03-14T05:03:57.3438755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3438913Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:03:57.3438977Z         
2025-03-14T05:03:57.3439233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3439719Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3439789Z         
2025-03-14T05:03:57.3440051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3441773Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3441848Z         
2025-03-14T05:03:57.3442123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3442285Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:03:57.3442356Z         
2025-03-14T05:03:57.3442600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3443084Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3443148Z         
2025-03-14T05:03:57.3443411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3445169Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3445260Z         
2025-03-14T05:03:57.3445548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3445684Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:03:57.3445758Z         
2025-03-14T05:03:57.3446004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3446486Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3446551Z         
2025-03-14T05:03:57.3446817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3448543Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3448625Z         
2025-03-14T05:03:57.3448908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3449058Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:03:57.3449128Z         
2025-03-14T05:03:57.3449406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3449563Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:03:57.3449629Z         
2025-03-14T05:03:57.3449878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3450344Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3450417Z         
2025-03-14T05:03:57.3450674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3452446Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3452536Z         
2025-03-14T05:03:57.3452814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3452954Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:03:57.3453020Z         
2025-03-14T05:03:57.3453270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3453736Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3453810Z         
2025-03-14T05:03:57.3454070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3455802Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3455901Z         
2025-03-14T05:03:57.3456184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3456329Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:03:57.3456402Z         
2025-03-14T05:03:57.3456653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3457157Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3457238Z         
2025-03-14T05:03:57.3457527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3459305Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3459397Z         
2025-03-14T05:03:57.3459660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3460156Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.3460229Z         
2025-03-14T05:03:57.3460498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3462343Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3462431Z         
2025-03-14T05:03:57.3462708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3462856Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:03:57.3462923Z         
2025-03-14T05:03:57.3463210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3463355Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:03:57.3463431Z         
2025-03-14T05:03:57.3463677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3464243Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3464317Z         
2025-03-14T05:03:57.3464602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3466422Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3466488Z         
2025-03-14T05:03:57.3466778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3466911Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:03:57.3466984Z         
2025-03-14T05:03:57.3467229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3467710Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3467776Z         
2025-03-14T05:03:57.3468039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3469779Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3469848Z         
2025-03-14T05:03:57.3470133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3470262Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:03:57.3470332Z         
2025-03-14T05:03:57.3470592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3471087Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3471173Z         
2025-03-14T05:03:57.3471436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3473179Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3473255Z         
2025-03-14T05:03:57.3473538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3473689Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:03:57.3473753Z         
2025-03-14T05:03:57.3474042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3474186Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:03:57.3474260Z         
2025-03-14T05:03:57.3474510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3474998Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3475062Z         
2025-03-14T05:03:57.3475328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3477074Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3477173Z         
2025-03-14T05:03:57.3477481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3477615Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:03:57.3477702Z         
2025-03-14T05:03:57.3477950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3478423Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3478486Z         
2025-03-14T05:03:57.3478756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3480460Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3480527Z         
2025-03-14T05:03:57.3480818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3480947Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:03:57.3481020Z         
2025-03-14T05:03:57.3481277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3481867Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3481936Z         
2025-03-14T05:03:57.3482206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3483986Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3484056Z         
2025-03-14T05:03:57.3484334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3484508Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:03:57.3484580Z         
2025-03-14T05:03:57.3484854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3485000Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:03:57.3485074Z         
2025-03-14T05:03:57.3485318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3485790Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3485855Z         
2025-03-14T05:03:57.3486120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3487833Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3487930Z         
2025-03-14T05:03:57.3488216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3488347Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:03:57.3488419Z         
2025-03-14T05:03:57.3488664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3489141Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3489206Z         
2025-03-14T05:03:57.3489469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3491207Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3491297Z         
2025-03-14T05:03:57.3491590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3491721Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:03:57.3491790Z         
2025-03-14T05:03:57.3492039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3492518Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3492585Z         
2025-03-14T05:03:57.3492854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3494575Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3494656Z         
2025-03-14T05:03:57.3494935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3495076Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:03:57.3495149Z         
2025-03-14T05:03:57.3495421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3495567Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:03:57.3495631Z         
2025-03-14T05:03:57.3495880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3496355Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3496429Z         
2025-03-14T05:03:57.3496697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3498417Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3498506Z         
2025-03-14T05:03:57.3498787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3498925Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:03:57.3498988Z         
2025-03-14T05:03:57.3499234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3499711Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3499778Z         
2025-03-14T05:03:57.3500041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3501778Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3501880Z         
2025-03-14T05:03:57.3502204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3502338Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:03:57.3502411Z         
2025-03-14T05:03:57.3502660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3503183Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3503250Z         
2025-03-14T05:03:57.3503517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3505341Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3505421Z         
2025-03-14T05:03:57.3505712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3505855Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:03:57.3505930Z         
2025-03-14T05:03:57.3506212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3506361Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:03:57.3506428Z         
2025-03-14T05:03:57.3506688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3507196Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3507295Z         
2025-03-14T05:03:57.3507579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3509375Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3509455Z         
2025-03-14T05:03:57.3509760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3509918Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:03:57.3509984Z         
2025-03-14T05:03:57.3510239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3510744Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3510816Z         
2025-03-14T05:03:57.3511086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3512867Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3512943Z         
2025-03-14T05:03:57.3513227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3513366Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:03:57.3513432Z         
2025-03-14T05:03:57.3513686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3514210Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3514276Z         
2025-03-14T05:03:57.3514545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3516357Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3516438Z         
2025-03-14T05:03:57.3516745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3516892Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:03:57.3516980Z         
2025-03-14T05:03:57.3517269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3517417Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:03:57.3517482Z         
2025-03-14T05:03:57.3517735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3518217Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3518294Z         
2025-03-14T05:03:57.3518555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3520277Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3520365Z         
2025-03-14T05:03:57.3520645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3520784Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:03:57.3520847Z         
2025-03-14T05:03:57.3521098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3521568Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3521640Z         
2025-03-14T05:03:57.3521898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3523700Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3523799Z         
2025-03-14T05:03:57.3524074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3524207Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:03:57.3524271Z         
2025-03-14T05:03:57.3524524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3524991Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3525063Z         
2025-03-14T05:03:57.3525317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3527038Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3527165Z         
2025-03-14T05:03:57.3527417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3527910Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.3527984Z         
2025-03-14T05:03:57.3528247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3530114Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3530207Z         
2025-03-14T05:03:57.3530495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3530644Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:03:57.3530709Z         
2025-03-14T05:03:57.3531003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3531149Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:03:57.3531228Z         
2025-03-14T05:03:57.3531481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3531969Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3532039Z         
2025-03-14T05:03:57.3532312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3534071Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3534162Z         
2025-03-14T05:03:57.3534449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3534578Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:03:57.3534654Z         
2025-03-14T05:03:57.3534894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3535371Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3535436Z         
2025-03-14T05:03:57.3535703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3537457Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3537540Z         
2025-03-14T05:03:57.3537825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3537954Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:03:57.3538027Z         
2025-03-14T05:03:57.3538268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3538755Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3538819Z         
2025-03-14T05:03:57.3539079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3540832Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3540915Z         
2025-03-14T05:03:57.3541203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3541356Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:03:57.3541431Z         
2025-03-14T05:03:57.3541710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3541858Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:03:57.3541924Z         
2025-03-14T05:03:57.3542180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3542698Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3542764Z         
2025-03-14T05:03:57.3543041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3544843Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3544932Z         
2025-03-14T05:03:57.3545236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3545379Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:03:57.3545457Z         
2025-03-14T05:03:57.3545720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3546224Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3546292Z         
2025-03-14T05:03:57.3546569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3548334Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3548411Z         
2025-03-14T05:03:57.3548712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3548845Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:03:57.3548918Z         
2025-03-14T05:03:57.3549175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3549675Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3549765Z         
2025-03-14T05:03:57.3550035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3551844Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3551913Z         
2025-03-14T05:03:57.3552202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3552364Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:03:57.3552437Z         
2025-03-14T05:03:57.3552716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3552861Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:03:57.3552926Z         
2025-03-14T05:03:57.3553184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3553767Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.3553840Z         
2025-03-14T05:03:57.3554088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3554638Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.3554715Z         
2025-03-14T05:03:57.3555123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.3555404Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:03:57.3555469Z         
2025-03-14T05:03:57.3555745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3556312Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.3556404Z         
2025-03-14T05:03:57.3556739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.3556937Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:03:57.3557001Z         
2025-03-14T05:03:57.3557253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3557811Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.3557879Z         
2025-03-14T05:03:57.3558275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.3558588Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:03:57.3558660Z         
2025-03-14T05:03:57.3558905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3559468Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.3559552Z         
2025-03-14T05:03:57.3559893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.3560095Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:03:57.3560168Z         
2025-03-14T05:03:57.3560411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3560979Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.3561053Z         
2025-03-14T05:03:57.3561438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.3561774Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:03:57.3561841Z         
2025-03-14T05:03:57.3562109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3562661Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.3562749Z         
2025-03-14T05:03:57.3563083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.3563299Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:03:57.3563365Z         
2025-03-14T05:03:57.3563620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3564214Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.3564287Z         
2025-03-14T05:03:57.3564645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:03:57.3564856Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:03:57.3564926Z         
2025-03-14T05:03:57.3565349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3565510Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.3565590Z         
2025-03-14T05:03:57.3565903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3566043Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:03:57.3566114Z         
2025-03-14T05:03:57.3566537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3566695Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.3566759Z         
2025-03-14T05:03:57.3567056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3567196Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:03:57.3567269Z         
2025-03-14T05:03:57.3567632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3567824Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:03:57.3567924Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:03:57.3568071Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:03:57.3568136Z         
2025-03-14T05:03:57.3568482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3568629Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:03:57.3568704Z         
2025-03-14T05:03:57.3569022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3569154Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:03:57.3569218Z         
2025-03-14T05:03:57.3569595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3569805Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:03:57.3569878Z         
2025-03-14T05:03:57.3570280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3570416Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:03:57.3570830Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:03:57.3570960Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:03:57.3571088Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:03:57.3571152Z         
2025-03-14T05:03:57.3571576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3571725Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.3571819Z         
2025-03-14T05:03:57.3572103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3572247Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:03:57.3572311Z         
2025-03-14T05:03:57.3572728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3572870Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.3572940Z         
2025-03-14T05:03:57.3573219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3573360Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:03:57.3573424Z         
2025-03-14T05:03:57.3573785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3573976Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:03:57.3574108Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:03:57.3574236Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:03:57.3574322Z         
2025-03-14T05:03:57.3574636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3574787Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:03:57.3574854Z         
2025-03-14T05:03:57.3575173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3575297Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:03:57.3575370Z         
2025-03-14T05:03:57.3575734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3575949Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:03:57.3576013Z         
2025-03-14T05:03:57.3576417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3576545Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:03:57.3576954Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:03:57.3577085Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:03:57.3577202Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:03:57.3577275Z         
2025-03-14T05:03:57.3577687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3577843Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.3577924Z         
2025-03-14T05:03:57.3578217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3578353Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:03:57.3578426Z         
2025-03-14T05:03:57.3578843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3578993Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.3579057Z         
2025-03-14T05:03:57.3579347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3579481Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:03:57.3579553Z         
2025-03-14T05:03:57.3579912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3580106Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:03:57.3580221Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:03:57.3580368Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:03:57.3580432Z         
2025-03-14T05:03:57.3580753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3580891Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:03:57.3580964Z         
2025-03-14T05:03:57.3581276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3581405Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:03:57.3581631Z         
2025-03-14T05:03:57.3582015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3582224Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:03:57.3582302Z         
2025-03-14T05:03:57.3582711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3582848Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:03:57.3583268Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:03:57.3583402Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:03:57.3583526Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:03:57.3583592Z         
2025-03-14T05:03:57.3584024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3584276Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.3584355Z         
2025-03-14T05:03:57.3584649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3584795Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:03:57.3584863Z         
2025-03-14T05:03:57.3585317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3585464Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.3585544Z         
2025-03-14T05:03:57.3585839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3585995Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:03:57.3586061Z         
2025-03-14T05:03:57.3586439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3586659Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:03:57.3586790Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:03:57.3586913Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:03:57.3586986Z         
2025-03-14T05:03:57.3587344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3587478Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:03:57.3587543Z         
2025-03-14T05:03:57.3587879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3588000Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:03:57.3588071Z         
2025-03-14T05:03:57.3588449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3588672Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:03:57.3588738Z         
2025-03-14T05:03:57.3589154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3589282Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:03:57.3589706Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:03:57.3589836Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:03:57.3589951Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:03:57.3590022Z         
2025-03-14T05:03:57.3590451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3590619Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.3590684Z         
2025-03-14T05:03:57.3590980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3591117Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:03:57.3591192Z         
2025-03-14T05:03:57.3591625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.3591777Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.3591841Z         
2025-03-14T05:03:57.3592135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3592271Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:03:57.3592342Z         
2025-03-14T05:03:57.3592710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.3592924Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:03:57.3593043Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:03:57.3593169Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:03:57.3593251Z         
2025-03-14T05:03:57.3593581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.3593707Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:03:57.3593782Z         
2025-03-14T05:03:57.3594099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.3594228Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:03:57.3594295Z         
2025-03-14T05:03:57.3594681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.3594888Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:03:57.3594966Z         
2025-03-14T05:03:57.3595371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.3595506Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:03:57.3595917Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:03:57.3596049Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:03:57.3596171Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:03:57.3596237Z         
2025-03-14T05:03:57.3596541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.3596696Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:03:57.3596834Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:03:57.3596961Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:03:57.3597088Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:03:57.3597208Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:03:57.3597283Z         
2025-03-14T05:03:57.3597541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3598050Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:03:57.3598116Z         
2025-03-14T05:03:57.3598396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3598594Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:03:57.3598664Z         
2025-03-14T05:03:57.3599071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3599590Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3599672Z         
2025-03-14T05:03:57.3600042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3600564Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:03:57.3600640Z         
2025-03-14T05:03:57.3600912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3601381Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:03:57.3601454Z         
2025-03-14T05:03:57.3601721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3601918Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:03:57.3601982Z         
2025-03-14T05:03:57.3602354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3602857Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3602946Z         
2025-03-14T05:03:57.3603289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3603796Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:03:57.3603864Z         
2025-03-14T05:03:57.3604124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3604600Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:03:57.3604667Z         
2025-03-14T05:03:57.3604946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3605130Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:03:57.3605219Z         
2025-03-14T05:03:57.3605606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3606114Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3606195Z         
2025-03-14T05:03:57.3606555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3607059Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:03:57.3607132Z         
2025-03-14T05:03:57.3607385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3607865Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:03:57.3607937Z         
2025-03-14T05:03:57.3608208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3608400Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:03:57.3608465Z         
2025-03-14T05:03:57.3608842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3609344Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.3609434Z         
2025-03-14T05:03:57.3609785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3610292Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:03:57.3610365Z         
2025-03-14T05:03:57.3610616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3611348Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:03:57.3611420Z         
2025-03-14T05:03:57.3611709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.3611894Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:03:57.3611964Z         
2025-03-14T05:03:57.3612318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.3613165Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:03:57.3613228Z         
2025-03-14T05:03:57.3613580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.3614361Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:03:57.3614434Z         
2025-03-14T05:03:57.3614770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:03:57.3614931Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:03:57.3615082Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:03:57.3615240Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:03:57.3615389Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:03:57.3615559Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:03:57.3615701Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:03:57.3615842Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:03:57.3615980Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:03:57.3616122Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:03:57.3616259Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:03:57.3616326Z         
2025-03-14T05:03:57.3616742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:03:57.3616921Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:03:57.3617112Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:03:57.3617287Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:03:57.3617472Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:03:57.3617666Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:03:57.3617844Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:03:57.3618013Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:03:57.3618185Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:03:57.3618348Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:03:57.3618496Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:03:57.3618659Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:03:57.3618820Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:03:57.3618963Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:03:57.3619117Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:03:57.3619285Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:03:57.3619350Z         
2025-03-14T05:03:57.3619762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3619968Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:03:57.3620041Z         
2025-03-14T05:03:57.3620472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3620637Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:03:57.3620802Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:03:57.3620951Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:03:57.3621018Z         
2025-03-14T05:03:57.3621396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3621566Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:03:57.3621642Z         
2025-03-14T05:03:57.3621955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3622106Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:03:57.3622170Z         
2025-03-14T05:03:57.3622489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3622621Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3622757Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3622909Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:03:57.3623000Z         
2025-03-14T05:03:57.3623338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3623476Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3623646Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3623810Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:03:57.3623878Z         
2025-03-14T05:03:57.3624272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3624406Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3624511Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:03:57.3624646Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:03:57.3624724Z         
2025-03-14T05:03:57.3625040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3625213Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:03:57.3625306Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:03:57.3625447Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:03:57.3625512Z         
2025-03-14T05:03:57.3625861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3626027Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3626147Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:03:57.3626219Z         
2025-03-14T05:03:57.3626516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3626678Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3626790Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:03:57.3626886Z         
2025-03-14T05:03:57.3627184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3627344Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3627457Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:03:57.3627528Z         
2025-03-14T05:03:57.3627827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3628020Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:03:57.3628135Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:03:57.3628242Z         
2025-03-14T05:03:57.3628575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3628728Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:03:57.3628793Z         
2025-03-14T05:03:57.3629140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3629293Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:03:57.3629370Z         
2025-03-14T05:03:57.3629712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3629875Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:03:57.3630001Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:03:57.3630164Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:03:57.3630304Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:03:57.3630378Z         
2025-03-14T05:03:57.3630723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3630873Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:03:57.3630997Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:03:57.3631156Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:03:57.3631302Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:03:57.3631367Z         
2025-03-14T05:03:57.3631706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3631828Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:03:57.3631997Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:03:57.3632134Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:03:57.3632207Z         
2025-03-14T05:03:57.3632538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3632666Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:03:57.3632854Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:03:57.3632999Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:03:57.3633065Z         
2025-03-14T05:03:57.3633378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3633479Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:03:57.3633609Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:03:57.3633675Z         
2025-03-14T05:03:57.3633998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3634099Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:03:57.3634224Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:03:57.3634293Z         
2025-03-14T05:03:57.3634602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3634722Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:03:57.3634877Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:03:57.3634944Z         
2025-03-14T05:03:57.3635269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3635385Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:03:57.3635537Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:03:57.3635604Z         
2025-03-14T05:03:57.3635959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3636139Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:03:57.3636211Z         
2025-03-14T05:03:57.3636547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3636719Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:03:57.3636783Z         
2025-03-14T05:03:57.3637182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3637357Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:03:57.3637430Z         
2025-03-14T05:03:57.3637822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3638039Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:03:57.3638103Z         
2025-03-14T05:03:57.3638531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3638688Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:03:57.3638861Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:03:57.3638999Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:03:57.3639072Z         
2025-03-14T05:03:57.3639435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3639610Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:03:57.3639681Z         
2025-03-14T05:03:57.3639983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3640137Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:03:57.3640201Z         
2025-03-14T05:03:57.3640515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3640646Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3640780Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3640928Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:03:57.3641016Z         
2025-03-14T05:03:57.3641347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3641479Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3641616Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3641769Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:03:57.3641833Z         
2025-03-14T05:03:57.3642134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3642254Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3642353Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:03:57.3642485Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:03:57.3642557Z         
2025-03-14T05:03:57.3642857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3643012Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:03:57.3643106Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:03:57.3643241Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:03:57.3643305Z         
2025-03-14T05:03:57.3643602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3643753Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3643874Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:03:57.3643937Z         
2025-03-14T05:03:57.3644236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3644386Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3644505Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:03:57.3644586Z         
2025-03-14T05:03:57.3644889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3645040Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3645163Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:03:57.3645231Z         
2025-03-14T05:03:57.3645536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3645725Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:03:57.3645841Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:03:57.3645919Z         
2025-03-14T05:03:57.3646245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3646393Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:03:57.3646460Z         
2025-03-14T05:03:57.3646804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3646955Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:03:57.3647028Z         
2025-03-14T05:03:57.3647361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3647520Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.3647649Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:03:57.3647810Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:03:57.3647950Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:03:57.3648021Z         
2025-03-14T05:03:57.3648358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3648501Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.3648626Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:03:57.3648784Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:03:57.3648922Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:03:57.3648992Z         
2025-03-14T05:03:57.3649311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3649433Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:03:57.3649593Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:03:57.3649738Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:03:57.3649800Z         
2025-03-14T05:03:57.3650129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3650260Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:03:57.3650434Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:03:57.3650565Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:03:57.3650637Z         
2025-03-14T05:03:57.3650939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3651045Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:03:57.3651163Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:03:57.3651236Z         
2025-03-14T05:03:57.3651530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3651633Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:03:57.3651747Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:03:57.3651820Z         
2025-03-14T05:03:57.3652110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3652234Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:03:57.3652399Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:03:57.3652486Z         
2025-03-14T05:03:57.3652781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3652918Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:03:57.3653050Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:03:57.3653122Z         
2025-03-14T05:03:57.3653457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3653654Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:03:57.3653726Z         
2025-03-14T05:03:57.3654047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3654214Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:03:57.3654278Z         
2025-03-14T05:03:57.3654658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3654833Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:03:57.3654907Z         
2025-03-14T05:03:57.3655296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3655509Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:03:57.3655572Z         
2025-03-14T05:03:57.3655999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3656165Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:03:57.3656324Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:03:57.3656458Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:03:57.3656528Z         
2025-03-14T05:03:57.3656890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3657066Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:03:57.3657131Z         
2025-03-14T05:03:57.3657441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3657584Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:03:57.3657658Z         
2025-03-14T05:03:57.3657962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3658095Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3658220Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3658390Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:03:57.3658474Z         
2025-03-14T05:03:57.3658789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3658931Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3659061Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3659212Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:03:57.3659285Z         
2025-03-14T05:03:57.3659585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3659713Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3659805Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:03:57.3659943Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:03:57.3660006Z         
2025-03-14T05:03:57.3660313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3660469Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:03:57.3660565Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:03:57.3660699Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:03:57.3660763Z         
2025-03-14T05:03:57.3661066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3661216Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3661338Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:03:57.3661401Z         
2025-03-14T05:03:57.3661695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3661844Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3661978Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:03:57.3662042Z         
2025-03-14T05:03:57.3662335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3662478Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3662595Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:03:57.3662660Z         
2025-03-14T05:03:57.3662960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3663138Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:03:57.3663255Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:03:57.3663319Z         
2025-03-14T05:03:57.3663651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3663787Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:03:57.3663859Z         
2025-03-14T05:03:57.3664303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3664458Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:03:57.3664543Z         
2025-03-14T05:03:57.3664896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3665032Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.3665168Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:03:57.3665323Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:03:57.3665478Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:03:57.3665543Z         
2025-03-14T05:03:57.3665898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3666044Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.3666167Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:03:57.3666329Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:03:57.3666466Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:03:57.3666540Z         
2025-03-14T05:03:57.3666873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3666998Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:03:57.3667161Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:03:57.3667305Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:03:57.3667372Z         
2025-03-14T05:03:57.3667712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3667844Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:03:57.3668014Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:03:57.3668163Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:03:57.3668235Z         
2025-03-14T05:03:57.3668547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3668654Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:03:57.3668771Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:03:57.3668845Z         
2025-03-14T05:03:57.3669179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3669287Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:03:57.3669400Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:03:57.3669470Z         
2025-03-14T05:03:57.3669772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3669914Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:03:57.3670065Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:03:57.3670140Z         
2025-03-14T05:03:57.3670441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3670581Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:03:57.3670715Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:03:57.3670788Z         
2025-03-14T05:03:57.3671127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3671323Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:03:57.3671389Z         
2025-03-14T05:03:57.3671723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3671886Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:03:57.3671960Z         
2025-03-14T05:03:57.3672338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3672520Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:03:57.3672585Z         
2025-03-14T05:03:57.3672987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3673193Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:03:57.3673268Z         
2025-03-14T05:03:57.3673698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3673872Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:03:57.3674032Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:03:57.3674169Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:03:57.3674243Z         
2025-03-14T05:03:57.3674620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3674804Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:03:57.3674872Z         
2025-03-14T05:03:57.3675196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3675344Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:03:57.3675416Z         
2025-03-14T05:03:57.3675730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3675867Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3676011Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3676187Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:03:57.3676254Z         
2025-03-14T05:03:57.3676579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3676721Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3676850Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3676998Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:03:57.3677071Z         
2025-03-14T05:03:57.3677377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3677505Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3677598Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:03:57.3677737Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:03:57.3677803Z         
2025-03-14T05:03:57.3678120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3678267Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:03:57.3678369Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:03:57.3678497Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:03:57.3678569Z         
2025-03-14T05:03:57.3678875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3679037Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3679150Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:03:57.3679226Z         
2025-03-14T05:03:57.3679527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3679701Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3679814Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:03:57.3679887Z         
2025-03-14T05:03:57.3680185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3680344Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3680466Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:03:57.3680531Z         
2025-03-14T05:03:57.3680841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3681024Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:03:57.3681146Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:03:57.3681213Z         
2025-03-14T05:03:57.3681700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3681841Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:03:57.3681963Z         
2025-03-14T05:03:57.3682306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3682449Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:03:57.3682539Z         
2025-03-14T05:03:57.3682892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3683032Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.3683168Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:03:57.3683326Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:03:57.3683480Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:03:57.3683547Z         
2025-03-14T05:03:57.3683908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3684044Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.3684174Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:03:57.3684325Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:03:57.3684472Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:03:57.3684540Z         
2025-03-14T05:03:57.3684924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3685039Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:03:57.3685207Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:03:57.3685341Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:03:57.3685416Z         
2025-03-14T05:03:57.3685767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3685884Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:03:57.3686044Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:03:57.3686180Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:03:57.3686244Z         
2025-03-14T05:03:57.3686552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3686650Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:03:57.3686774Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:03:57.3686838Z         
2025-03-14T05:03:57.3687145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3687238Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:03:57.3687358Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:03:57.3687421Z         
2025-03-14T05:03:57.3687742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3687875Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:03:57.3688014Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:03:57.3688093Z         
2025-03-14T05:03:57.3688403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3688526Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:03:57.3688653Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:03:57.3688726Z         
2025-03-14T05:03:57.3689069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3689263Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:03:57.3689328Z         
2025-03-14T05:03:57.3689664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3689825Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:03:57.3689899Z         
2025-03-14T05:03:57.3690274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3690450Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:03:57.3690512Z         
2025-03-14T05:03:57.3690904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.3691104Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:03:57.3691176Z         
2025-03-14T05:03:57.3691590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.3691761Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:03:57.3691907Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:03:57.3692048Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:03:57.3692112Z         
2025-03-14T05:03:57.3692499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.3692661Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:03:57.3692733Z         
2025-03-14T05:03:57.3693037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.3693186Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:03:57.3693250Z         
2025-03-14T05:03:57.3693563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.3693703Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:03:57.3693834Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3693996Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:03:57.3694071Z         
2025-03-14T05:03:57.3694403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.3694531Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:03:57.3694649Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:03:57.3694803Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:03:57.3694866Z         
2025-03-14T05:03:57.3695176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.3695302Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.3695392Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:03:57.3695527Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:03:57.3695592Z         
2025-03-14T05:03:57.3695900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.3696044Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:03:57.3696143Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:03:57.3696268Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:03:57.3696339Z         
2025-03-14T05:03:57.3696632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.3696788Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.3696898Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:03:57.3696970Z         
2025-03-14T05:03:57.3697257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.3697425Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.3697534Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:03:57.3697604Z         
2025-03-14T05:03:57.3697893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.3698045Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.3698152Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:03:57.3698225Z         
2025-03-14T05:03:57.3698519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.3698704Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:03:57.3698811Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:03:57.3698880Z         
2025-03-14T05:03:57.3699206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.3699361Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:03:57.3699426Z         
2025-03-14T05:03:57.3699805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.3699954Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:03:57.3700029Z         
2025-03-14T05:03:57.3700365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.3700506Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.3700625Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:03:57.3700784Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:03:57.3700920Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:03:57.3700992Z         
2025-03-14T05:03:57.3701334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.3701468Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.3701593Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:03:57.3701739Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:03:57.3701878Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:03:57.3701940Z         
2025-03-14T05:03:57.3702272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.3702386Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:03:57.3702548Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:03:57.3702681Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:03:57.3702772Z         
2025-03-14T05:03:57.3703104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.3703225Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:03:57.3703391Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:03:57.3703529Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:03:57.3703594Z         
2025-03-14T05:03:57.3703910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.3704010Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:03:57.3704184Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:03:57.3704260Z         
2025-03-14T05:03:57.3704578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.3704678Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:03:57.3704801Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:03:57.3704867Z         
2025-03-14T05:03:57.3705204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.3705341Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:03:57.3705483Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:03:57.3705578Z         
2025-03-14T05:03:57.3705900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.3706017Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:03:57.3706153Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:03:57.3706217Z         
2025-03-14T05:03:57.3706560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.3706748Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:03:57.3706821Z         
2025-03-14T05:03:57.3707152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.3707318Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:03:57.3707385Z         
2025-03-14T05:03:57.3707767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.3707939Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:03:57.3708014Z         
2025-03-14T05:03:57.3708489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:03:57.3708633Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:03:57.3708699Z         
2025-03-14T05:03:57.3709002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3709172Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:03:57.3709239Z         
2025-03-14T05:03:57.3709677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3709796Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:03:57.3709906Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:03:57.3710021Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:03:57.3710095Z         
2025-03-14T05:03:57.3710550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3710696Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3710925Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:03:57.3710998Z         
2025-03-14T05:03:57.3711464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3711655Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3711722Z         
2025-03-14T05:03:57.3712023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3712160Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:03:57.3712235Z         
2025-03-14T05:03:57.3712666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3712794Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:03:57.3712905Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:03:57.3713037Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:03:57.3713106Z         
2025-03-14T05:03:57.3713571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3713707Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3713949Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:03:57.3714016Z         
2025-03-14T05:03:57.3714478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3714648Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3714726Z         
2025-03-14T05:03:57.3715023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3715160Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:03:57.3715244Z         
2025-03-14T05:03:57.3715682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3715803Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:03:57.3715909Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:03:57.3716034Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:03:57.3716099Z         
2025-03-14T05:03:57.3716562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3716699Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3716948Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:03:57.3717014Z         
2025-03-14T05:03:57.3717475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3717655Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3717731Z         
2025-03-14T05:03:57.3718073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3718221Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:03:57.3718288Z         
2025-03-14T05:03:57.3718718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3718836Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:03:57.3718952Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:03:57.3719072Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:03:57.3719150Z         
2025-03-14T05:03:57.3719599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3719742Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.3719991Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:03:57.3720066Z         
2025-03-14T05:03:57.3720499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3720668Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3720734Z         
2025-03-14T05:03:57.3721029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3721150Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:03:57.3721227Z         
2025-03-14T05:03:57.3721638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.3721771Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:03:57.3721881Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:03:57.3721992Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:03:57.3722055Z         
2025-03-14T05:03:57.3722507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.3722672Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:03:57.3722903Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:03:57.3722975Z         
2025-03-14T05:03:57.3723414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.3723577Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.3723643Z         
2025-03-14T05:03:57.3723950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.3724086Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:03:57.3724159Z         
2025-03-14T05:03:57.3724440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3724805Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:03:57.3724870Z         
2025-03-14T05:03:57.3725143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3725582Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:03:57.3725655Z         
2025-03-14T05:03:57.3725917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.3726117Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:03:57.3726181Z         
2025-03-14T05:03:57.3726561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:03:57.3726705Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:03:57.3726770Z         
2025-03-14T05:03:57.3727061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.3727215Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:03:57.3727287Z         
2025-03-14T05:03:57.3727648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:03:57.3727807Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:03:57.3727870Z         
2025-03-14T05:03:57.3728352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:03:57.3728491Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:03:57.3728618Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:03:57.3728770Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:03:57.3728910Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:03:57.3728974Z         
2025-03-14T05:03:57.3729345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:03:57.3729459Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:03:57.3729532Z         
2025-03-14T05:03:57.3729538Z 
2025-03-14T05:03:57.3729630Z class GraphModule(torch.nn.Module):
2025-03-14T05:03:57.3792160Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:03:57.3792743Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:03:57.3793168Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3793662Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3794126Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3794581Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3795021Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3795422Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3795900Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3796373Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3797451Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3797935Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3798371Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3798888Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3799403Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3799855Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3800302Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3800674Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3801087Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3801481Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3801894Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3802272Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3802641Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3803057Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3803463Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3803861Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3804241Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3804587Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3804999Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3805390Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3805769Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3806149Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3806501Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3806900Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3807307Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3808091Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3808493Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3808868Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3809296Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3809701Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3810078Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3810452Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3810803Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3811201Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3811604Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3811980Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3812357Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3812714Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3813118Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3813513Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3813882Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3814253Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3814591Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3815026Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3815419Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3815826Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3816248Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3816632Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3817074Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3817459Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3817843Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3818207Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3818555Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3818956Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3819358Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3819738Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3820101Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3820447Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3820840Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3821253Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3821649Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3822028Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3822392Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3822804Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3823218Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3823617Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3824024Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3824472Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3824937Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3825390Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3825779Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3826154Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3826506Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3826902Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3827303Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3827675Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3828112Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3828455Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3828876Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3829278Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3829649Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3830019Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3830356Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3830757Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3831144Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3831522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3831891Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3832245Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3832646Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3833038Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3833414Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3833775Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3834119Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3834557Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3834946Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3835340Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3835704Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3836054Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3836446Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3836853Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3837230Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3837595Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3837941Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3838336Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3838750Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3839124Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3839506Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3839849Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3840250Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3840664Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3841051Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3841441Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3841781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3842182Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3842575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3842958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3843331Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3843671Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3844077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3844470Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3844864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3845228Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3845579Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3845980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3846373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3846755Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3847153Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3847517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3847941Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3848424Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3848899Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3849351Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3849776Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3850198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3850615Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3851025Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3851437Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3851840Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3852276Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3852718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3853143Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3853556Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3853960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3854419Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3854879Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3855297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3855717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3856101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3856558Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3856967Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3857348Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3857724Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3858063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3858483Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3858870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3859250Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3859614Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3859960Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3860357Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3860778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3861159Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3861544Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3861893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3862298Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3862683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3863062Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3863426Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3863772Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3864271Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3864739Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3865198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3865619Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3865986Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3866383Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3866782Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3867174Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3867562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3867929Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3868325Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3868720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3869093Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3869465Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3869806Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3870209Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3870608Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3870974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3871366Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3871702Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3872106Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3872493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3872873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3873240Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3873593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3874015Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3874415Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3874800Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3875163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3875507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3875906Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3876296Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3876688Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3877095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3877461Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3877870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3878272Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3878652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3879017Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3879363Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3879752Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3880175Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3880547Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3880940Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3881287Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3881808Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3882210Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3882582Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3882956Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3883293Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3883697Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3884090Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3884503Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3884871Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3885224Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:03:57.3885637Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3886034Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3886454Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:03:57.3886870Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:03:57.3887238Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3887640Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3888028Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3888409Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3888782Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3889121Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3889523Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3889912Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3890293Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3890671Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3891016Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3891416Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3891808Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3892193Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3892552Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3892927Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:03:57.3893332Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3893742Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3894123Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:03:57.3894487Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:03:57.3894835Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:03:57.3895229Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3895630Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3896001Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:03:57.3896378Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:03:57.3896722Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:03:57.3897131Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:03:57.3897531Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:03:57.3897904Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:03:57.3898276Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:03:57.3898500Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:03:57.3898723Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:03:57.3898970Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:03:57.3899228Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:03:57.3899479Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:03:57.3899708Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:03:57.3899929Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:03:57.3900135Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:03:57.3900358Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:03:57.3900568Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:03:57.3900788Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:03:57.3900990Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:03:57.3901215Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:03:57.3901427Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:03:57.3901650Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:03:57.3901863Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:03:57.3902218Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:03:57.3902562Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:03:57.3903358Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:03:57.3904181Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:03:57.3904969Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:03:57.3905734Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:03:57.3906442Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:03:57.3907166Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:03:57.3907933Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:03:57.3908709Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:03:57.3909464Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:03:57.3909946Z         
2025-03-14T05:03:57.3910324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3911172Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3911911Z         
2025-03-14T05:03:57.3912270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3914267Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3916040Z         
2025-03-14T05:03:57.3916417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:03:57.3916883Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:03:57.3917139Z         
2025-03-14T05:03:57.3917573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:03:57.3918221Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:03:57.3918566Z         
2025-03-14T05:03:57.3918902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3919682Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3920264Z         
2025-03-14T05:03:57.3920614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3922682Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3924527Z         
2025-03-14T05:03:57.3924900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3925378Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:03:57.3925634Z         
2025-03-14T05:03:57.3925972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3926765Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3927363Z         
2025-03-14T05:03:57.3927712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3929787Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3931638Z         
2025-03-14T05:03:57.3932003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3932477Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:03:57.3932735Z         
2025-03-14T05:03:57.3933071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3933869Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3934475Z         
2025-03-14T05:03:57.3934828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3936930Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3938817Z         
2025-03-14T05:03:57.3939166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3940095Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.3940793Z         
2025-03-14T05:03:57.3941204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3943461Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3945658Z         
2025-03-14T05:03:57.3946045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3946533Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:03:57.3946803Z         
2025-03-14T05:03:57.3947181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3947681Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:03:57.3947956Z         
2025-03-14T05:03:57.3948300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3949163Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3949804Z         
2025-03-14T05:03:57.3950157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3952325Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3954222Z         
2025-03-14T05:03:57.3954602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3955108Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:03:57.3955369Z         
2025-03-14T05:03:57.3955694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3956474Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3957068Z         
2025-03-14T05:03:57.3957414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3959463Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3961354Z         
2025-03-14T05:03:57.3961723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3962198Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:03:57.3962456Z         
2025-03-14T05:03:57.3962789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3963579Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3964173Z         
2025-03-14T05:03:57.3964546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3966630Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3968487Z         
2025-03-14T05:03:57.3968856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3969346Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:03:57.3969620Z         
2025-03-14T05:03:57.3969984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3970467Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:03:57.3970737Z         
2025-03-14T05:03:57.3971074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3971851Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3972438Z         
2025-03-14T05:03:57.3972782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3974857Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3976689Z         
2025-03-14T05:03:57.3977056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3977535Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:03:57.3977802Z         
2025-03-14T05:03:57.3978162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3978955Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.3979567Z         
2025-03-14T05:03:57.3979913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3982075Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3984004Z         
2025-03-14T05:03:57.3984438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3984939Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:03:57.3985209Z         
2025-03-14T05:03:57.3985551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3986361Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.3987023Z         
2025-03-14T05:03:57.3987369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3989461Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3991317Z         
2025-03-14T05:03:57.3991672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.3992191Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:03:57.3992482Z         
2025-03-14T05:03:57.3992856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.3993364Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:03:57.3993636Z         
2025-03-14T05:03:57.3993970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.3994752Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.3995349Z         
2025-03-14T05:03:57.3995698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.3997770Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.3999617Z         
2025-03-14T05:03:57.3999987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4000471Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:03:57.4000763Z         
2025-03-14T05:03:57.4001099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4001910Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4002515Z         
2025-03-14T05:03:57.4002867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4004978Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4006840Z         
2025-03-14T05:03:57.4007204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4007679Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:03:57.4007936Z         
2025-03-14T05:03:57.4008258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4009064Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4009670Z         
2025-03-14T05:03:57.4010016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4012077Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4013922Z         
2025-03-14T05:03:57.4014280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4015070Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.4015681Z         
2025-03-14T05:03:57.4016028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4018167Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4020225Z         
2025-03-14T05:03:57.4020589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4021085Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:03:57.4021348Z         
2025-03-14T05:03:57.4021716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4022204Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:03:57.4022472Z         
2025-03-14T05:03:57.4022807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4023591Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4024285Z         
2025-03-14T05:03:57.4024696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4026778Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4028631Z         
2025-03-14T05:03:57.4028997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4029472Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:03:57.4029734Z         
2025-03-14T05:03:57.4030065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4030851Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4031451Z         
2025-03-14T05:03:57.4031797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4033884Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4035734Z         
2025-03-14T05:03:57.4036101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4036587Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:03:57.4036856Z         
2025-03-14T05:03:57.4037193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4037985Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4038589Z         
2025-03-14T05:03:57.4038936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4040986Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4042849Z         
2025-03-14T05:03:57.4043201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4043693Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:03:57.4043962Z         
2025-03-14T05:03:57.4044335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4044816Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:03:57.4045085Z         
2025-03-14T05:03:57.4045418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4046213Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4046804Z         
2025-03-14T05:03:57.4047161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4049223Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4051047Z         
2025-03-14T05:03:57.4051412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4051888Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:03:57.4052144Z         
2025-03-14T05:03:57.4052468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4053246Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4053836Z         
2025-03-14T05:03:57.4054177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4056228Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4058083Z         
2025-03-14T05:03:57.4058447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4058923Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:03:57.4059180Z         
2025-03-14T05:03:57.4059506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4060322Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4060935Z         
2025-03-14T05:03:57.4061278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4063332Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4065259Z         
2025-03-14T05:03:57.4065642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4066155Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:03:57.4066429Z         
2025-03-14T05:03:57.4066837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4067371Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:03:57.4067660Z         
2025-03-14T05:03:57.4068011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4068882Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4069568Z         
2025-03-14T05:03:57.4069945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4072086Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4073969Z         
2025-03-14T05:03:57.4074388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4074879Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:03:57.4075173Z         
2025-03-14T05:03:57.4075519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4076389Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4077024Z         
2025-03-14T05:03:57.4077398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4079544Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4081409Z         
2025-03-14T05:03:57.4081892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4082377Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:03:57.4082640Z         
2025-03-14T05:03:57.4082976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4083799Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4084393Z         
2025-03-14T05:03:57.4084734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4086831Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4088658Z         
2025-03-14T05:03:57.4089021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4089526Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:03:57.4089797Z         
2025-03-14T05:03:57.4090158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4090637Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:03:57.4090903Z         
2025-03-14T05:03:57.4091232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4092003Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4092575Z         
2025-03-14T05:03:57.4092917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4094967Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4096805Z         
2025-03-14T05:03:57.4097175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4097642Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:03:57.4097898Z         
2025-03-14T05:03:57.4098232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4099016Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4099609Z         
2025-03-14T05:03:57.4099952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4102042Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4103881Z         
2025-03-14T05:03:57.4104304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4104798Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:03:57.4105058Z         
2025-03-14T05:03:57.4105403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4106194Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4106807Z         
2025-03-14T05:03:57.4107155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4109251Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4111161Z         
2025-03-14T05:03:57.4111507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4112354Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.4113013Z         
2025-03-14T05:03:57.4113387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4115592Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4117681Z         
2025-03-14T05:03:57.4118065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4118562Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:03:57.4118820Z         
2025-03-14T05:03:57.4119202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4119704Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:03:57.4119956Z         
2025-03-14T05:03:57.4120287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4121053Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4121630Z         
2025-03-14T05:03:57.4121969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4123988Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4125828Z         
2025-03-14T05:03:57.4126194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4126662Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:03:57.4126915Z         
2025-03-14T05:03:57.4127244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4128023Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4128609Z         
2025-03-14T05:03:57.4128976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4131032Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4132845Z         
2025-03-14T05:03:57.4133208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4133675Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:03:57.4133926Z         
2025-03-14T05:03:57.4134254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4135019Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4135600Z         
2025-03-14T05:03:57.4135942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4137973Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4139810Z         
2025-03-14T05:03:57.4140175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4140648Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:03:57.4140911Z         
2025-03-14T05:03:57.4141274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4141743Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:03:57.4141998Z         
2025-03-14T05:03:57.4142344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4143119Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4143724Z         
2025-03-14T05:03:57.4144072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4146279Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4148152Z         
2025-03-14T05:03:57.4148524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4149013Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:03:57.4149271Z         
2025-03-14T05:03:57.4149612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4150430Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4151061Z         
2025-03-14T05:03:57.4151416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4153534Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4155396Z         
2025-03-14T05:03:57.4155768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4156276Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:03:57.4156533Z         
2025-03-14T05:03:57.4156887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4157680Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4158306Z         
2025-03-14T05:03:57.4158662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4160748Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4162621Z         
2025-03-14T05:03:57.4162986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4163471Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:03:57.4163738Z         
2025-03-14T05:03:57.4164102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4164581Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:03:57.4164867Z         
2025-03-14T05:03:57.4165208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4166002Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4166601Z         
2025-03-14T05:03:57.4166949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4169097Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4170979Z         
2025-03-14T05:03:57.4171352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4171901Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:03:57.4172157Z         
2025-03-14T05:03:57.4172486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4173262Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4173847Z         
2025-03-14T05:03:57.4174189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4176212Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4178018Z         
2025-03-14T05:03:57.4178383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4178868Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:03:57.4179116Z         
2025-03-14T05:03:57.4179445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4180222Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4180808Z         
2025-03-14T05:03:57.4181148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4183319Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4185247Z         
2025-03-14T05:03:57.4185611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4186088Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:03:57.4186348Z         
2025-03-14T05:03:57.4186709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4187178Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:03:57.4187433Z         
2025-03-14T05:03:57.4187764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4188522Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4189102Z         
2025-03-14T05:03:57.4189440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4191476Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4193339Z         
2025-03-14T05:03:57.4193705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4194168Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:03:57.4194421Z         
2025-03-14T05:03:57.4194753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4195540Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4196128Z         
2025-03-14T05:03:57.4196485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4198560Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4200384Z         
2025-03-14T05:03:57.4200749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4201210Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:03:57.4201462Z         
2025-03-14T05:03:57.4201792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4202567Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4203153Z         
2025-03-14T05:03:57.4203496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4205532Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4207378Z         
2025-03-14T05:03:57.4207748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4208221Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:03:57.4208484Z         
2025-03-14T05:03:57.4208848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4209319Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:03:57.4209575Z         
2025-03-14T05:03:57.4209908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4210719Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4211298Z         
2025-03-14T05:03:57.4211641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4213679Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4215485Z         
2025-03-14T05:03:57.4215847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4216307Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:03:57.4216559Z         
2025-03-14T05:03:57.4216888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4217661Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4218248Z         
2025-03-14T05:03:57.4218591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4220648Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4222462Z         
2025-03-14T05:03:57.4222827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4223291Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:03:57.4223548Z         
2025-03-14T05:03:57.4223917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4224800Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4225454Z         
2025-03-14T05:03:57.4225851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4228038Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4230578Z         
2025-03-14T05:03:57.4231023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4231586Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:03:57.4231868Z         
2025-03-14T05:03:57.4232249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4232745Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:03:57.4233012Z         
2025-03-14T05:03:57.4233342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4234141Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4234722Z         
2025-03-14T05:03:57.4235068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4237116Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4238978Z         
2025-03-14T05:03:57.4239789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4240289Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:03:57.4240572Z         
2025-03-14T05:03:57.4240916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4241717Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4242329Z         
2025-03-14T05:03:57.4242677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4244699Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4246524Z         
2025-03-14T05:03:57.4246888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4247351Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:03:57.4247621Z         
2025-03-14T05:03:57.4247949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4248726Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4249315Z         
2025-03-14T05:03:57.4249658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4251732Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4253558Z         
2025-03-14T05:03:57.4253888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4254688Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:03:57.4255279Z         
2025-03-14T05:03:57.4255620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4257706Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4259733Z         
2025-03-14T05:03:57.4260098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4260564Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:03:57.4260821Z         
2025-03-14T05:03:57.4261178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4261673Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:03:57.4261929Z         
2025-03-14T05:03:57.4262257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4263029Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4263619Z         
2025-03-14T05:03:57.4263966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4266136Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4268021Z         
2025-03-14T05:03:57.4268396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4268872Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:03:57.4269127Z         
2025-03-14T05:03:57.4269464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4270262Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4270860Z         
2025-03-14T05:03:57.4271209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4273293Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4275184Z         
2025-03-14T05:03:57.4275556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4276027Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:03:57.4276283Z         
2025-03-14T05:03:57.4276620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4277421Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4278027Z         
2025-03-14T05:03:57.4278381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4280467Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4282456Z         
2025-03-14T05:03:57.4282825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4283304Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:03:57.4283572Z         
2025-03-14T05:03:57.4283929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4284403Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:03:57.4284657Z         
2025-03-14T05:03:57.4284988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4285753Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:03:57.4286331Z         
2025-03-14T05:03:57.4286667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4288686Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4288795Z         
2025-03-14T05:03:57.4289087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4289223Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:03:57.4289298Z         
2025-03-14T05:03:57.4289544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4290347Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:03:57.4290431Z         
2025-03-14T05:03:57.4290739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4292502Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4292608Z         
2025-03-14T05:03:57.4292891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4293037Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:03:57.4293106Z         
2025-03-14T05:03:57.4293357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4293834Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:03:57.4293909Z         
2025-03-14T05:03:57.4294168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:03:57.4295916Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:03:57.4296004Z         
2025-03-14T05:03:57.4296290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:03:57.4296453Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:03:57.4296519Z         
2025-03-14T05:03:57.4296813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:03:57.4296955Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:03:57.4297026Z         
2025-03-14T05:03:57.4297307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4297884Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.4297966Z         
2025-03-14T05:03:57.4298215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4298752Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:03:57.4298824Z         
2025-03-14T05:03:57.4299242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.4299504Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:03:57.4299578Z         
2025-03-14T05:03:57.4299827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4300400Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.4300777Z         
2025-03-14T05:03:57.4301189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.4301390Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:03:57.4301487Z         
2025-03-14T05:03:57.4301737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4302308Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:03:57.4302374Z         
2025-03-14T05:03:57.4302787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.4303112Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:03:57.4303179Z         
2025-03-14T05:03:57.4303434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4304032Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.4304107Z         
2025-03-14T05:03:57.4304512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.4304771Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:03:57.4304840Z         
2025-03-14T05:03:57.4305110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4305712Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:03:57.4305784Z         
2025-03-14T05:03:57.4306181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:03:57.4306508Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:03:57.4306579Z         
2025-03-14T05:03:57.4306826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4307403Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.4307468Z         
2025-03-14T05:03:57.4307812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:03:57.4308038Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:03:57.4308111Z         
2025-03-14T05:03:57.4308360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4308988Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:03:57.4309054Z         
2025-03-14T05:03:57.4309421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:03:57.4309637Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:03:57.4309713Z         
2025-03-14T05:03:57.4310146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4310328Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.4310395Z         
2025-03-14T05:03:57.4310709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4310874Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:03:57.4310940Z         
2025-03-14T05:03:57.4311661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4311835Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:03:57.4311915Z         
2025-03-14T05:03:57.4312211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4312363Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:03:57.4312430Z         
2025-03-14T05:03:57.4312809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.4312992Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:03:57.4313106Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:03:57.4313229Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:03:57.4313301Z         
2025-03-14T05:03:57.4313629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.4313767Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:03:57.4313834Z         
2025-03-14T05:03:57.4314168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.4314292Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:03:57.4314367Z         
2025-03-14T05:03:57.4314745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.4314996Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:03:57.4315060Z         
2025-03-14T05:03:57.4315487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.4315612Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:03:57.4316033Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:03:57.4316159Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:03:57.4316285Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:03:57.4316350Z         
2025-03-14T05:03:57.4316788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4316955Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.4317029Z         
2025-03-14T05:03:57.4317341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4317524Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:03:57.4317589Z         
2025-03-14T05:03:57.4318019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4318175Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:03:57.4318240Z         
2025-03-14T05:03:57.4318534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4318674Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:03:57.4318745Z         
2025-03-14T05:03:57.4319108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.4319312Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:03:57.4319418Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:03:57.4319554Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:03:57.4319618Z         
2025-03-14T05:03:57.4319949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.4320081Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:03:57.4320154Z         
2025-03-14T05:03:57.4320475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.4320610Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:03:57.4320677Z         
2025-03-14T05:03:57.4321058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.4321291Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:03:57.4321364Z         
2025-03-14T05:03:57.4321776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.4321917Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:03:57.4322331Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:03:57.4322470Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:03:57.4322587Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:03:57.4322660Z         
2025-03-14T05:03:57.4323085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4323250Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.4323334Z         
2025-03-14T05:03:57.4323623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4323782Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:03:57.4323847Z         
2025-03-14T05:03:57.4324263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4324402Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:03:57.4324474Z         
2025-03-14T05:03:57.4324753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4324893Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:03:57.4324958Z         
2025-03-14T05:03:57.4325321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.4325511Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:03:57.4325617Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:03:57.4325734Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:03:57.4325805Z         
2025-03-14T05:03:57.4326118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.4326249Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:03:57.4326315Z         
2025-03-14T05:03:57.4326635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.4326757Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:03:57.4326845Z         
2025-03-14T05:03:57.4327211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.4327425Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:03:57.4327488Z         
2025-03-14T05:03:57.4327897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.4328022Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:03:57.4328433Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:03:57.4328555Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:03:57.4328678Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:03:57.4328741Z         
2025-03-14T05:03:57.4329175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4329341Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.4329415Z         
2025-03-14T05:03:57.4329698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4329855Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:03:57.4329918Z         
2025-03-14T05:03:57.4330332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4330479Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:03:57.4330542Z         
2025-03-14T05:03:57.4330831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4330961Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:03:57.4331033Z         
2025-03-14T05:03:57.4331383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.4331577Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:03:57.4331675Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:03:57.4332074Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:03:57.4332153Z         
2025-03-14T05:03:57.4332511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.4332640Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:03:57.4332713Z         
2025-03-14T05:03:57.4333027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.4333157Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:03:57.4333244Z         
2025-03-14T05:03:57.4333622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.4333831Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:03:57.4333902Z         
2025-03-14T05:03:57.4334302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.4334436Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:03:57.4334839Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:03:57.4334971Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:03:57.4335082Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:03:57.4335153Z         
2025-03-14T05:03:57.4335585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4335791Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.4335856Z         
2025-03-14T05:03:57.4336164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4336307Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:03:57.4336371Z         
2025-03-14T05:03:57.4336792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:03:57.4336933Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:03:57.4337004Z         
2025-03-14T05:03:57.4337284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4337423Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:03:57.4337488Z         
2025-03-14T05:03:57.4337865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:03:57.4338056Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:03:57.4338172Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:03:57.4338287Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:03:57.4338357Z         
2025-03-14T05:03:57.4338675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:03:57.4338809Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:03:57.4338872Z         
2025-03-14T05:03:57.4339201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:03:57.4339338Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:03:57.4339413Z         
2025-03-14T05:03:57.4339790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:03:57.4340004Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:03:57.4340069Z         
2025-03-14T05:03:57.4340484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:03:57.4340608Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:03:57.4341031Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:03:57.4341153Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:03:57.4341276Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:03:57.4341339Z         
2025-03-14T05:03:57.4341662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.4341808Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:03:57.4341948Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:03:57.4342089Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:03:57.4342220Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:03:57.4342343Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:03:57.4342418Z         
2025-03-14T05:03:57.4343476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4344026Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:03:57.4344109Z         
2025-03-14T05:03:57.4344444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.4344662Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:03:57.4344731Z         
2025-03-14T05:03:57.4345140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.4345658Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.4345732Z         
2025-03-14T05:03:57.4346092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.4346602Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:03:57.4346694Z         
2025-03-14T05:03:57.4346954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4347423Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:03:57.4347496Z         
2025-03-14T05:03:57.4347775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.4347965Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:03:57.4348039Z         
2025-03-14T05:03:57.4348402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.4348931Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.4348996Z         
2025-03-14T05:03:57.4349357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.4349867Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:03:57.4349941Z         
2025-03-14T05:03:57.4350185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4350658Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:03:57.4350721Z         
2025-03-14T05:03:57.4350994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.4351176Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:03:57.4351245Z         
2025-03-14T05:03:57.4351608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.4352104Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.4352174Z         
2025-03-14T05:03:57.4352513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.4352999Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:03:57.4353079Z         
2025-03-14T05:03:57.4353332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4353791Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:03:57.4353864Z         
2025-03-14T05:03:57.4354132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.4354314Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:03:57.4354377Z         
2025-03-14T05:03:57.4354746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.4355273Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:03:57.4355340Z         
2025-03-14T05:03:57.4355696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.4356192Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:03:57.4356263Z         
2025-03-14T05:03:57.4356505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:03:57.4357245Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:03:57.4357311Z         
2025-03-14T05:03:57.4357584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:03:57.4357752Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:03:57.4357823Z         
2025-03-14T05:03:57.4358181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:03:57.4359010Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:03:57.4359097Z         
2025-03-14T05:03:57.4359439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:03:57.4360243Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:03:57.4360307Z         
2025-03-14T05:03:57.4360640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:03:57.4360803Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:03:57.4360951Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:03:57.4361109Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:03:57.4361274Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:03:57.4361448Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:03:57.4361584Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:03:57.4361751Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:03:57.4361882Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:03:57.4362031Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:03:57.4362161Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:03:57.4362232Z         
2025-03-14T05:03:57.4362643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:03:57.4362824Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:03:57.4363001Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:03:57.4363184Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:03:57.4363346Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:03:57.4363520Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:03:57.4363688Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:03:57.4363843Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:03:57.4364004Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:03:57.4364176Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:03:57.4364317Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:03:57.4364495Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:03:57.4364657Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:03:57.4364801Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:03:57.4364956Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:03:57.4365123Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:03:57.4365186Z         
2025-03-14T05:03:57.4365591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.4365798Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:03:57.4365864Z         
2025-03-14T05:03:57.4366298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.4366465Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:03:57.4366637Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:03:57.4366776Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:03:57.4366864Z         
2025-03-14T05:03:57.4367228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.4367403Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:03:57.4367466Z         
2025-03-14T05:03:57.4367775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.4367916Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:03:57.4367986Z         
2025-03-14T05:03:57.4368291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.4368428Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:03:57.4368554Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4368712Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:03:57.4368776Z         
2025-03-14T05:03:57.4369089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.4369212Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:03:57.4369339Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:03:57.4369486Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:03:57.4369556Z         
2025-03-14T05:03:57.4369854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.4369983Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4370137Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:03:57.4370270Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:03:57.4370334Z         
2025-03-14T05:03:57.4370638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.4370781Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:03:57.4370881Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:03:57.4371009Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:03:57.4371078Z         
2025-03-14T05:03:57.4371399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.4371564Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.4371680Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:03:57.4371752Z         
2025-03-14T05:03:57.4372041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.4372201Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.4372329Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:03:57.4372415Z         
2025-03-14T05:03:57.4372713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.4372878Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.4372997Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:03:57.4373060Z         
2025-03-14T05:03:57.4373362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.4373541Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:03:57.4373659Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:03:57.4373721Z         
2025-03-14T05:03:57.4374056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.4374196Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:03:57.4374264Z         
2025-03-14T05:03:57.4374584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.4374725Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:03:57.4374785Z         
2025-03-14T05:03:57.4375122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.4375259Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:03:57.4375390Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:03:57.4375537Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:03:57.4375682Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:03:57.4375771Z         
2025-03-14T05:03:57.4376113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.4376250Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:03:57.4376378Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:03:57.4376527Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:03:57.4376669Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:03:57.4376731Z         
2025-03-14T05:03:57.4377059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.4377179Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:03:57.4377344Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:03:57.4377473Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:03:57.4377544Z         
2025-03-14T05:03:57.4377866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.4378007Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:03:57.4378187Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:03:57.4378331Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:03:57.4378410Z         
2025-03-14T05:03:57.4378730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.4378839Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:03:57.4378960Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:03:57.4379026Z         
2025-03-14T05:03:57.4379344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.4379450Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:03:57.4379570Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:03:57.4379644Z         
2025-03-14T05:03:57.4379950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.4380075Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:03:57.4380209Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:03:57.4380280Z         
2025-03-14T05:03:57.4380583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.4380707Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:03:57.4380838Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:03:57.4380914Z         
2025-03-14T05:03:57.4381258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.4381586Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:03:57.4381703Z         
2025-03-14T05:03:57.4382045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.4382210Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:03:57.4382283Z         
2025-03-14T05:03:57.4382669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.4382865Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:03:57.4382932Z         
2025-03-14T05:03:57.4383349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.4383580Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:03:57.4383654Z         
2025-03-14T05:03:57.4384094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.4384351Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:03:57.4384533Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:03:57.4384688Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:03:57.4384780Z         
2025-03-14T05:03:57.4385170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.4385347Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:03:57.4385423Z         
2025-03-14T05:03:57.4385740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.4385901Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:03:57.4385965Z         
2025-03-14T05:03:57.4386303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.4386438Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:03:57.4386577Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4386730Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:03:57.4386801Z         
2025-03-14T05:03:57.4387115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.4387248Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:03:57.4387380Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:03:57.4387534Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:03:57.4387606Z         
2025-03-14T05:03:57.4387913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.4388047Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4388160Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:03:57.4388301Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:03:57.4388367Z         
2025-03-14T05:03:57.4388681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.4388832Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:03:57.4388935Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:03:57.4389067Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:03:57.4389138Z         
2025-03-14T05:03:57.4389437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.4389600Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.4389717Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:03:57.4389790Z         
2025-03-14T05:03:57.4390087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.4390264Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.4390379Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:03:57.4390465Z         
2025-03-14T05:03:57.4390757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.4390932Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.4391047Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:03:57.4391116Z         
2025-03-14T05:03:57.4391413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.4391605Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:03:57.4391717Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:03:57.4391788Z         
2025-03-14T05:03:57.4392122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.4392272Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:03:57.4392336Z         
2025-03-14T05:03:57.4392673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.4392812Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:03:57.4392884Z         
2025-03-14T05:03:57.4393222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.4393369Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.4393504Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:03:57.4393660Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:03:57.4393811Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:03:57.4393892Z         
2025-03-14T05:03:57.4394246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.4394385Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:03:57.4394517Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:03:57.4394672Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:03:57.4394822Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:03:57.4394887Z         
2025-03-14T05:03:57.4395225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.4395341Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:03:57.4395509Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:03:57.4395645Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:03:57.4395718Z         
2025-03-14T05:03:57.4396076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.4396213Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:03:57.4396382Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:03:57.4396549Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:03:57.4396612Z         
2025-03-14T05:03:57.4396927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.4397027Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:03:57.4397153Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:03:57.4397216Z         
2025-03-14T05:03:57.4397528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.4397624Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:03:57.4397746Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:03:57.4397811Z         
2025-03-14T05:03:57.4398118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.4398238Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:03:57.4398379Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:03:57.4398443Z         
2025-03-14T05:03:57.4398751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.4398868Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:03:57.4399009Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:03:57.4399072Z         
2025-03-14T05:03:57.4399418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.4399613Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:03:57.4399702Z         
2025-03-14T05:03:57.4400038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.4400210Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:03:57.4400274Z         
2025-03-14T05:03:57.4400667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.4400852Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:03:57.4400919Z         
2025-03-14T05:03:57.4401325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.4401533Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:03:57.4401604Z         
2025-03-14T05:03:57.4402053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.4402230Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:03:57.4402382Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:03:57.4402543Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:03:57.4402608Z         
2025-03-14T05:03:57.4402995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.4403159Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:03:57.4403230Z         
2025-03-14T05:03:57.4403528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.4403675Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:03:57.4403739Z         
2025-03-14T05:03:57.4404043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.4404169Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:03:57.4404298Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4404441Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:03:57.4404511Z         
2025-03-14T05:03:57.4404811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.4404938Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:03:57.4405056Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:03:57.4405208Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:03:57.4405274Z         
2025-03-14T05:03:57.4405577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.4405711Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4405806Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:03:57.4405933Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:03:57.4406001Z         
2025-03-14T05:03:57.4406301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.4406453Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:03:57.4406545Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:03:57.4406678Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:03:57.4406743Z         
2025-03-14T05:03:57.4407039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.4407190Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.4407309Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:03:57.4407372Z         
2025-03-14T05:03:57.4407666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.4407833Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.4407961Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:03:57.4408032Z         
2025-03-14T05:03:57.4408330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.4408484Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.4408592Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:03:57.4408664Z         
2025-03-14T05:03:57.4408952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.4409140Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:03:57.4409248Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:03:57.4409320Z         
2025-03-14T05:03:57.4409648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.4409798Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:03:57.4409863Z         
2025-03-14T05:03:57.4410198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.4410336Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:03:57.4410408Z         
2025-03-14T05:03:57.4410746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.4410890Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.4411015Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:03:57.4411178Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:03:57.4411335Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:03:57.4411408Z         
2025-03-14T05:03:57.4411765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.4411909Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:03:57.4412029Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:03:57.4412185Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:03:57.4412317Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:03:57.4412391Z         
2025-03-14T05:03:57.4412715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.4412838Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:03:57.4412993Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:03:57.4413134Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:03:57.4413196Z         
2025-03-14T05:03:57.4413559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.4413678Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:03:57.4413854Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:03:57.4413991Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:03:57.4414056Z         
2025-03-14T05:03:57.4414361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.4414458Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:03:57.4414579Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:03:57.4414641Z         
2025-03-14T05:03:57.4414947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.4415041Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:03:57.4415160Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:03:57.4415224Z         
2025-03-14T05:03:57.4415521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.4415636Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:03:57.4415776Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:03:57.4415840Z         
2025-03-14T05:03:57.4416139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.4416250Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:03:57.4416385Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:03:57.4431754Z         
2025-03-14T05:03:57.4432400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.4432714Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:03:57.4432780Z         
2025-03-14T05:03:57.4433131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.4433310Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:03:57.4433377Z         
2025-03-14T05:03:57.4433770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.4433955Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:03:57.4434017Z         
2025-03-14T05:03:57.4434424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.4434629Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:03:57.4434697Z         
2025-03-14T05:03:57.4435158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.4435350Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:03:57.4435509Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:03:57.4435690Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:03:57.4435760Z         
2025-03-14T05:03:57.4436143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.4436316Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:03:57.4436403Z         
2025-03-14T05:03:57.4436712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.4436871Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:03:57.4436936Z         
2025-03-14T05:03:57.4437253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.4437386Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:03:57.4437521Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4437669Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:03:57.4437741Z         
2025-03-14T05:03:57.4438052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.4438186Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:03:57.4438305Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:03:57.4438463Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:03:57.4438526Z         
2025-03-14T05:03:57.4438832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.4438982Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4439072Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:03:57.4439209Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:03:57.4439273Z         
2025-03-14T05:03:57.4439582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.4439729Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:03:57.4439830Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:03:57.4439959Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:03:57.4440029Z         
2025-03-14T05:03:57.4440341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.4440496Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.4440610Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:03:57.4440680Z         
2025-03-14T05:03:57.4440991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.4441155Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.4441273Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:03:57.4441353Z         
2025-03-14T05:03:57.4441647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.4441795Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.4441911Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:03:57.4441974Z         
2025-03-14T05:03:57.4442270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.4442449Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:03:57.4442565Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:03:57.4442628Z         
2025-03-14T05:03:57.4442962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.4443100Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:03:57.4443169Z         
2025-03-14T05:03:57.4443488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.4443630Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:03:57.4443693Z         
2025-03-14T05:03:57.4444037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.4444170Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.4444305Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:03:57.4444458Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:03:57.4444620Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:03:57.4444684Z         
2025-03-14T05:03:57.4445028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.4445163Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:03:57.4445297Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:03:57.4445446Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:03:57.4445591Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:03:57.4445655Z         
2025-03-14T05:03:57.4445987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.4446101Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:03:57.4446264Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:03:57.4446406Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:03:57.4446521Z         
2025-03-14T05:03:57.4446865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.4446977Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:03:57.4447163Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:03:57.4447293Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:03:57.4447365Z         
2025-03-14T05:03:57.4447669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.4447777Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:03:57.4447895Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:03:57.4447968Z         
2025-03-14T05:03:57.4448273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.4448372Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:03:57.4448488Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:03:57.4448561Z         
2025-03-14T05:03:57.4448863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.4448990Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:03:57.4449129Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:03:57.4449205Z         
2025-03-14T05:03:57.4449516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.4449639Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:03:57.4449768Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:03:57.4449844Z         
2025-03-14T05:03:57.4450181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.4450389Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:03:57.4450453Z         
2025-03-14T05:03:57.4450779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.4450937Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:03:57.4451009Z         
2025-03-14T05:03:57.4451378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.4451554Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:03:57.4451620Z         
2025-03-14T05:03:57.4452010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:03:57.4452211Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:03:57.4452284Z         
2025-03-14T05:03:57.4452730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:03:57.4452886Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:03:57.4453050Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:03:57.4453188Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:03:57.4453252Z         
2025-03-14T05:03:57.4453621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:03:57.4453792Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:03:57.4453856Z         
2025-03-14T05:03:57.4454168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:03:57.4454309Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:03:57.4454380Z         
2025-03-14T05:03:57.4454683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:03:57.4454816Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:03:57.4454936Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4455086Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:03:57.4455150Z         
2025-03-14T05:03:57.4455465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:03:57.4455586Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:03:57.4455708Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:03:57.4455852Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:03:57.4455923Z         
2025-03-14T05:03:57.4456220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:03:57.4456362Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:03:57.4456450Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:03:57.4456583Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:03:57.4456646Z         
2025-03-14T05:03:57.4456954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:03:57.4457094Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:03:57.4457191Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:03:57.4457313Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:03:57.4457387Z         
2025-03-14T05:03:57.4457677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:03:57.4457834Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:03:57.4457946Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:03:57.4458018Z         
2025-03-14T05:03:57.4458338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:03:57.4458507Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:03:57.4458616Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:03:57.4458704Z         
2025-03-14T05:03:57.4458992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:03:57.4459146Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:03:57.4459252Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:03:57.4459324Z         
2025-03-14T05:03:57.4459614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:03:57.4459800Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:03:57.4459905Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:03:57.4459978Z         
2025-03-14T05:03:57.4460300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:03:57.4460441Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:03:57.4460512Z         
2025-03-14T05:03:57.4460830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:03:57.4460967Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:03:57.4461032Z         
2025-03-14T05:03:57.4461369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:03:57.4461500Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.4461628Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:03:57.4461793Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:03:57.4461933Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:03:57.4461996Z         
2025-03-14T05:03:57.4462338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:03:57.4462471Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:03:57.4462596Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:03:57.4462740Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:03:57.4462879Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:03:57.4462944Z         
2025-03-14T05:03:57.4463268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:03:57.4463381Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:03:57.4463540Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:03:57.4463683Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:03:57.4463755Z         
2025-03-14T05:03:57.4464098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:03:57.4464321Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:03:57.4464485Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:03:57.4464629Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:03:57.4464695Z         
2025-03-14T05:03:57.4465012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:03:57.4465112Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:03:57.4465238Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:03:57.4465302Z         
2025-03-14T05:03:57.4465618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:03:57.4465715Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:03:57.4465838Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:03:57.4465905Z         
2025-03-14T05:03:57.4466214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:03:57.4466328Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:03:57.4466467Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:03:57.4466531Z         
2025-03-14T05:03:57.4466843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:03:57.4466955Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:03:57.4467087Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:03:57.4467152Z         
2025-03-14T05:03:57.4467504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:03:57.4467724Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:03:57.4467790Z         
2025-03-14T05:03:57.4468129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:03:57.4468288Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:03:57.4468361Z         
2025-03-14T05:03:57.4468742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:03:57.4468924Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:03:57.4468990Z         
2025-03-14T05:03:57.4469472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:03:57.4469609Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:03:57.4469681Z         
2025-03-14T05:03:57.4469991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4470158Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:03:57.4470224Z         
2025-03-14T05:03:57.4470681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.4470794Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:03:57.4470905Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:03:57.4471017Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:03:57.4471088Z         
2025-03-14T05:03:57.4471544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.4471685Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.4471917Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:03:57.4471993Z         
2025-03-14T05:03:57.4472442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.4472619Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.4472684Z         
2025-03-14T05:03:57.4472987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4473107Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:03:57.4473180Z         
2025-03-14T05:03:57.4473608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.4473732Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:03:57.4473854Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:03:57.4473977Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:03:57.4474041Z         
2025-03-14T05:03:57.4474501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.4474638Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.4474875Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:03:57.4474949Z         
2025-03-14T05:03:57.4475396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.4475570Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.4475638Z         
2025-03-14T05:03:57.4475940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4476080Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:03:57.4476153Z         
2025-03-14T05:03:57.4476597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.4476733Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:03:57.4476839Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:03:57.4476964Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:03:57.4477029Z         
2025-03-14T05:03:57.4477485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.4477617Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.4477867Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:03:57.4477930Z         
2025-03-14T05:03:57.4478368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.4478530Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.4478602Z         
2025-03-14T05:03:57.4478881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4479008Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:03:57.4479072Z         
2025-03-14T05:03:57.4479490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.4479600Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:03:57.4479710Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:03:57.4479824Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:03:57.4479919Z         
2025-03-14T05:03:57.4480354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.4480489Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:03:57.4480721Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:03:57.4480785Z         
2025-03-14T05:03:57.4481223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.4481383Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.4481686Z         
2025-03-14T05:03:57.4481976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4482104Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:03:57.4482169Z         
2025-03-14T05:03:57.4482672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:03:57.4482783Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:03:57.4482922Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:03:57.4483035Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:03:57.4483107Z         
2025-03-14T05:03:57.4483544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:03:57.4483708Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:03:57.4483933Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:03:57.4484004Z         
2025-03-14T05:03:57.4484441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:03:57.4484605Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:03:57.4484669Z         
2025-03-14T05:03:57.4484960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:03:57.4485079Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:03:57.4485151Z         
2025-03-14T05:03:57.4485422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.4485795Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:03:57.4485859Z         
2025-03-14T05:03:57.4486138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.4486612Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:03:57.4486676Z         
2025-03-14T05:03:57.4486953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:03:57.4487147Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:03:57.4487217Z         
2025-03-14T05:03:57.4487586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:03:57.4487736Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:03:57.4487799Z         
2025-03-14T05:03:57.4488091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:03:57.4488235Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:03:57.4488306Z         
2025-03-14T05:03:57.4488699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:03:57.4488838Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:03:57.4488901Z         
2025-03-14T05:03:57.4489386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:03:57.4489521Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:03:57.4489643Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:03:57.4489792Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:03:57.4489929Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:03:57.4489994Z         
2025-03-14T05:03:57.4490353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:03:57.4490466Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:03:57.4490538Z         
2025-03-14T05:04:09.6738112Z 
2025-03-14T05:04:09.6742214Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:09.6745035Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:04:09.6748220Z         l_features_p2_ = L_features_p2_
2025-03-14T05:04:09.6753752Z         l_features_p3_ = L_features_p3_
2025-03-14T05:04:09.6756441Z         l_features_p4_ = L_features_p4_
2025-03-14T05:04:09.6756825Z         l_features_p5_ = L_features_p5_
2025-03-14T05:04:09.6762024Z         l_features_p6_ = L_features_p6_
2025-03-14T05:04:09.6764292Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:04:09.6765040Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:04:09.6771621Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:04:09.6780123Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:04:09.6786744Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:04:09.6787590Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:04:09.6788247Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:04:09.6790541Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:04:09.6791160Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:04:09.6791771Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:04:09.6792343Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:04:09.6792725Z         
2025-03-14T05:04:09.6793350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6794063Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:04:09.6794337Z         
2025-03-14T05:04:09.6794744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6795266Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:04:09.6795532Z         
2025-03-14T05:04:09.6796070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6796725Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:04:09.6797004Z         
2025-03-14T05:04:09.6797403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6797928Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:04:09.6798193Z         
2025-03-14T05:04:09.6798654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.6799310Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:04:09.6799649Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:04:09.6799929Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:04:09.6800194Z         
2025-03-14T05:04:09.6800617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.6801133Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:04:09.6801384Z         
2025-03-14T05:04:09.6801799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.6802301Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:04:09.6802546Z         
2025-03-14T05:04:09.6803003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.6803667Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:04:09.6803997Z         
2025-03-14T05:04:09.6804524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.6805141Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:04:09.6805660Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:04:09.6806178Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:04:09.6806483Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:04:09.6806728Z         
2025-03-14T05:04:09.6807282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6807949Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:04:09.6808228Z         
2025-03-14T05:04:09.6808628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6809134Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:04:09.6809404Z         
2025-03-14T05:04:09.6809945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6810607Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:04:09.6810885Z         
2025-03-14T05:04:09.6811287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6811790Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:04:09.6812065Z         
2025-03-14T05:04:09.6812552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.6813225Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:04:09.6813595Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:04:09.6813896Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:04:09.6814141Z         
2025-03-14T05:04:09.6814563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.6815079Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:04:09.6815335Z         
2025-03-14T05:04:09.6815747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.6816264Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:04:09.6816512Z         
2025-03-14T05:04:09.6816968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.6817644Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:04:09.6817970Z         
2025-03-14T05:04:09.6818483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.6819159Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:04:09.6819660Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:04:09.6820156Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:04:09.6820459Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:04:09.6820688Z         
2025-03-14T05:04:09.6821213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6821844Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:04:09.6822113Z         
2025-03-14T05:04:09.6822495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6822989Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:04:09.6823248Z         
2025-03-14T05:04:09.6823761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6824655Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:04:09.6824959Z         
2025-03-14T05:04:09.6825395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6825883Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:04:09.6826144Z         
2025-03-14T05:04:09.6826612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.6827259Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:04:09.6827615Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:04:09.6827888Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:04:09.6828127Z         
2025-03-14T05:04:09.6828543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.6829048Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:04:09.6829296Z         
2025-03-14T05:04:09.6829704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.6830213Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:04:09.6830454Z         
2025-03-14T05:04:09.6830922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.6831597Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:04:09.6831946Z         
2025-03-14T05:04:09.6832503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.6833116Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:04:09.6833621Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:04:09.6834118Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:04:09.6834427Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:04:09.6834669Z         
2025-03-14T05:04:09.6835209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6835860Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:04:09.6836136Z         
2025-03-14T05:04:09.6836525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6837028Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:04:09.6837296Z         
2025-03-14T05:04:09.6837824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6838468Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:04:09.6838742Z         
2025-03-14T05:04:09.6839128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6839625Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:04:09.6839889Z         
2025-03-14T05:04:09.6840354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.6841011Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:04:09.6841370Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:04:09.6841654Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:04:09.6841904Z         
2025-03-14T05:04:09.6842327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.6842850Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:04:09.6843109Z         
2025-03-14T05:04:09.6843535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.6844046Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:04:09.6844294Z         
2025-03-14T05:04:09.6844772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.6845437Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:04:09.6845772Z         
2025-03-14T05:04:09.6846278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.6846894Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:04:09.6847381Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:04:09.6847859Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:04:09.6848158Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:04:09.6848383Z         
2025-03-14T05:04:09.6848900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6849530Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:04:09.6849793Z         
2025-03-14T05:04:09.6850175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6850660Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:04:09.6850919Z         
2025-03-14T05:04:09.6851431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.6852056Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:04:09.6852325Z         
2025-03-14T05:04:09.6852709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.6853195Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:04:09.6853454Z         
2025-03-14T05:04:09.6853905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.6854540Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:04:09.6854892Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:04:09.6855170Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:04:09.6855411Z         
2025-03-14T05:04:09.6855833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.6856347Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:04:09.6856594Z         
2025-03-14T05:04:09.6857002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.6857508Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:04:09.6857752Z         
2025-03-14T05:04:09.6858213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.6858871Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:04:09.6859197Z         
2025-03-14T05:04:09.6859721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.6860325Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:04:09.6860813Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:04:09.6861295Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:04:09.6861588Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:04:09.6861823Z         
2025-03-14T05:04:09.6862209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:04:09.6862681Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:04:09.6862986Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:04:09.6863292Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:04:09.6863583Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:04:09.6863886Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:04:09.6864253Z         
2025-03-14T05:04:09.6864668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.6865481Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:04:09.6866040Z         
2025-03-14T05:04:09.6866411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.6866952Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:04:09.6867304Z         
2025-03-14T05:04:09.6867786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.6868648Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.6869197Z         
2025-03-14T05:04:09.6869660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.6870512Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:04:09.6871057Z         
2025-03-14T05:04:09.6871415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.6872154Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:04:09.6872715Z         
2025-03-14T05:04:09.6873126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.6873662Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:04:09.6873991Z         
2025-03-14T05:04:09.6874461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.6875305Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.6875854Z         
2025-03-14T05:04:09.6876311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.6877147Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:04:09.6877682Z         
2025-03-14T05:04:09.6878035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.6878749Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:04:09.6879278Z         
2025-03-14T05:04:09.6879637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.6880142Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:04:09.6880447Z         
2025-03-14T05:04:09.6880890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.6882265Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.6882795Z         
2025-03-14T05:04:09.6883244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.6884054Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:04:09.6884570Z         
2025-03-14T05:04:09.6884912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.6885617Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:04:09.6886128Z         
2025-03-14T05:04:09.6886480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.6887058Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:04:09.6887364Z         
2025-03-14T05:04:09.6887851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.6888701Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.6889217Z         
2025-03-14T05:04:09.6889667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.6890472Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:04:09.6890981Z         
2025-03-14T05:04:09.6891322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.6892233Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:04:09.6892944Z         
2025-03-14T05:04:09.6893323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.6893848Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:04:09.6894149Z         
2025-03-14T05:04:09.6894619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.6895687Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:04:09.6896461Z         
2025-03-14T05:04:09.6896903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.6897896Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:04:09.6898601Z         
2025-03-14T05:04:09.6899017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:04:09.6899586Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:04:09.6899945Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:04:09.6900303Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:04:09.6900692Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:04:09.6901063Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:04:09.6901415Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:04:09.6901775Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:04:09.6902120Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:04:09.6902456Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:04:09.6902788Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:04:09.6903046Z         
2025-03-14T05:04:09.6903570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:04:09.6904315Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:04:09.6904791Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:04:09.6905258Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:04:09.6905689Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:04:09.6906079Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:04:09.6906481Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:04:09.6906854Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:04:09.6907214Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:04:09.6907598Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:04:09.6907990Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:04:09.6908351Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:04:09.6908730Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:04:09.6909098Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:04:09.6909450Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:04:09.6909827Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:04:09.6910113Z         
2025-03-14T05:04:09.6910616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.6911273Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:04:09.6911605Z         
2025-03-14T05:04:09.6912126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.6912790Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:04:09.6913170Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:04:09.6913519Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:04:09.6913805Z         
2025-03-14T05:04:09.6914269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.6914877Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:04:09.6915168Z         
2025-03-14T05:04:09.6915576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.6916090Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:04:09.6916357Z         
2025-03-14T05:04:09.6916759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.6917272Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:09.6917591Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:09.6917932Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:09.6918203Z         
2025-03-14T05:04:09.6918616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.6919121Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:09.6919432Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:09.6919770Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:04:09.6920047Z         
2025-03-14T05:04:09.6920456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.6920962Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:09.6921265Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:04:09.6921535Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:04:09.6921785Z         
2025-03-14T05:04:09.6922180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.6922693Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:09.6922990Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:04:09.6923271Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:04:09.6923527Z         
2025-03-14T05:04:09.6923947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.6924511Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.6924834Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:04:09.6925073Z         
2025-03-14T05:04:09.6925457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.6925996Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.6926340Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:04:09.6926583Z         
2025-03-14T05:04:09.6926964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.6927468Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.6927786Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:04:09.6928017Z         
2025-03-14T05:04:09.6928398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.6928920Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:09.6929264Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:04:09.6929499Z         
2025-03-14T05:04:09.6929918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.6930454Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:09.6930714Z         
2025-03-14T05:04:09.6931129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.6931653Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:09.6931908Z         
2025-03-14T05:04:09.6932340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.6932878Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:09.6933199Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:04:09.6933535Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:09.6933882Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:04:09.6934170Z         
2025-03-14T05:04:09.6934617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.6935147Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:09.6935470Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:04:09.6935809Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:09.6936160Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:04:09.6936422Z         
2025-03-14T05:04:09.6936847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.6937358Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:09.6937695Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:09.6938046Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:04:09.6938310Z         
2025-03-14T05:04:09.6938751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.6939274Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:09.6939616Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:09.6939995Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:04:09.6940257Z         
2025-03-14T05:04:09.6940660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.6941133Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:09.6941402Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:09.6941639Z         
2025-03-14T05:04:09.6942041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.6942513Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:09.6942780Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:09.6943020Z         
2025-03-14T05:04:09.6943414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.6943918Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:09.6944301Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:09.6944566Z         
2025-03-14T05:04:09.6944967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.6945477Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:09.6945795Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:09.6946063Z         
2025-03-14T05:04:09.6946523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.6947108Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:09.6947434Z         
2025-03-14T05:04:09.6947864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.6948418Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:04:09.6948707Z         
2025-03-14T05:04:09.6949180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.6949813Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:04:09.6950115Z         
2025-03-14T05:04:09.6950621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.6951289Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:04:09.6951624Z         
2025-03-14T05:04:09.6952142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.6952801Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:04:09.6953181Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:04:09.6953533Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:04:09.6953811Z         
2025-03-14T05:04:09.6954273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.6954865Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:04:09.6955153Z         
2025-03-14T05:04:09.6955550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.6956060Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:04:09.6956330Z         
2025-03-14T05:04:09.6956734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.6957236Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:04:09.6957550Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:09.6957890Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:04:09.6958158Z         
2025-03-14T05:04:09.6958564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.6959060Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:04:09.6959372Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:04:09.6959707Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:04:09.6959982Z         
2025-03-14T05:04:09.6960383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.6960882Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:09.6961181Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:04:09.6961463Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:04:09.6961718Z         
2025-03-14T05:04:09.6962115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.6962638Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:04:09.6962940Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:04:09.6963221Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:04:09.6963477Z         
2025-03-14T05:04:09.6963868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.6964391Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.6964710Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:04:09.6964951Z         
2025-03-14T05:04:09.6965341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.6965862Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.6966209Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:04:09.6966451Z         
2025-03-14T05:04:09.6966836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.6967356Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.6967680Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:04:09.6967917Z         
2025-03-14T05:04:09.6968311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.6968860Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:04:09.6969214Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:04:09.6969455Z         
2025-03-14T05:04:09.6969887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.6970428Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:04:09.6970692Z         
2025-03-14T05:04:09.6971117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.6971644Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:04:09.6971903Z         
2025-03-14T05:04:09.6972336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.6972877Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:04:09.6973200Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:04:09.6973544Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:04:09.6973935Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:04:09.6974197Z         
2025-03-14T05:04:09.6974635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.6975173Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:04:09.6975499Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:04:09.6975838Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:04:09.6976189Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:04:09.6976453Z         
2025-03-14T05:04:09.6976884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.6977394Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:04:09.6977731Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:04:09.6978086Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:04:09.6978346Z         
2025-03-14T05:04:09.6978813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.6979323Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:04:09.6979663Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:04:09.6980043Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:04:09.6980305Z         
2025-03-14T05:04:09.6980703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.6981168Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:04:09.6982236Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:04:09.6982680Z         
2025-03-14T05:04:09.6983125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.6983615Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:04:09.6983893Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:04:09.6984179Z         
2025-03-14T05:04:09.6984718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.6986165Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:04:09.6986526Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:04:09.6986819Z         
2025-03-14T05:04:09.6987263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.6987777Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:04:09.6989046Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:04:09.6989348Z         
2025-03-14T05:04:09.6989834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.6990569Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:04:09.6990899Z         
2025-03-14T05:04:09.6991352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.6992538Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:04:09.6993927Z         
2025-03-14T05:04:09.6994449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.6995816Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:04:09.6996172Z         
2025-03-14T05:04:09.6996887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.6998379Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:04:09.6998841Z         
2025-03-14T05:04:09.6999675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7000369Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:04:09.7000744Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:04:09.7001152Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:04:09.7001424Z         
2025-03-14T05:04:09.7001899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7003003Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:04:09.7003307Z         
2025-03-14T05:04:09.7003731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7004275Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:04:09.7004554Z         
2025-03-14T05:04:09.7004973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7005489Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7005814Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7006156Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:04:09.7006436Z         
2025-03-14T05:04:09.7006853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7007368Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7007683Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7008026Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:04:09.7008313Z         
2025-03-14T05:04:09.7008727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7009259Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7009534Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:04:09.7009807Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:04:09.7010061Z         
2025-03-14T05:04:09.7010460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7010972Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:04:09.7011275Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:04:09.7011549Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:04:09.7011798Z         
2025-03-14T05:04:09.7012202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7012704Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7013035Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:04:09.7013269Z         
2025-03-14T05:04:09.7013839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7014378Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7014717Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:04:09.7014993Z         
2025-03-14T05:04:09.7015384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7015890Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7016215Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:04:09.7016453Z         
2025-03-14T05:04:09.7016845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7017396Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:04:09.7017765Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:04:09.7018003Z         
2025-03-14T05:04:09.7018430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7018961Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:04:09.7019225Z         
2025-03-14T05:04:09.7019647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7020182Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:04:09.7020447Z         
2025-03-14T05:04:09.7020901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7021448Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:04:09.7021780Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:04:09.7022121Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:04:09.7022510Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:04:09.7022779Z         
2025-03-14T05:04:09.7023229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7023788Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:04:09.7024193Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:04:09.7024555Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:04:09.7024916Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:04:09.7025193Z         
2025-03-14T05:04:09.7025637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7026171Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:04:09.7026531Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:04:09.7026900Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:04:09.7027194Z         
2025-03-14T05:04:09.7027646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7028166Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:04:09.7028544Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:04:09.7028909Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:04:09.7029173Z         
2025-03-14T05:04:09.7029600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7030082Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:04:09.7030364Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:04:09.7030612Z         
2025-03-14T05:04:09.7031017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7031488Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:04:09.7031763Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:04:09.7032009Z         
2025-03-14T05:04:09.7032406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7032893Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:04:09.7033212Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:04:09.7033479Z         
2025-03-14T05:04:09.7033888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7034369Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:04:09.7034675Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:04:09.7034940Z         
2025-03-14T05:04:09.7035383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7036020Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:04:09.7036333Z         
2025-03-14T05:04:09.7036765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7037329Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:04:09.7037622Z         
2025-03-14T05:04:09.7038103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7038727Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:04:09.7039032Z         
2025-03-14T05:04:09.7039528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7040453Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:04:09.7040791Z         
2025-03-14T05:04:09.7041359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7042023Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:04:09.7042399Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:04:09.7042743Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:04:09.7043005Z         
2025-03-14T05:04:09.7043464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7044050Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:04:09.7044337Z         
2025-03-14T05:04:09.7044734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7045250Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:04:09.7045522Z         
2025-03-14T05:04:09.7045919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7046421Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7046730Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7047060Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:04:09.7047330Z         
2025-03-14T05:04:09.7047733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7048234Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7048542Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7048875Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:04:09.7049150Z         
2025-03-14T05:04:09.7049550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7050070Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7050344Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:04:09.7050623Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:04:09.7050882Z         
2025-03-14T05:04:09.7051283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7051799Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:04:09.7052100Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:04:09.7052379Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:04:09.7052633Z         
2025-03-14T05:04:09.7053032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7053545Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7053870Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:04:09.7054102Z         
2025-03-14T05:04:09.7054521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7055021Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7055362Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:04:09.7055596Z         
2025-03-14T05:04:09.7055974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7056473Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7056792Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:04:09.7057028Z         
2025-03-14T05:04:09.7057418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7057956Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:04:09.7058305Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:04:09.7058542Z         
2025-03-14T05:04:09.7058962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7059490Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:04:09.7059751Z         
2025-03-14T05:04:09.7060164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7060688Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:04:09.7060947Z         
2025-03-14T05:04:09.7061372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7061905Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:04:09.7062223Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:04:09.7062585Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:04:09.7062936Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:04:09.7063198Z         
2025-03-14T05:04:09.7063630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7064276Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:04:09.7064621Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:04:09.7064963Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:04:09.7065327Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:04:09.7065598Z         
2025-03-14T05:04:09.7066037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7066559Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:04:09.7066901Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:04:09.7067290Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:04:09.7067563Z         
2025-03-14T05:04:09.7068012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7068545Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:04:09.7068886Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:04:09.7069256Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:04:09.7069523Z         
2025-03-14T05:04:09.7069929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7070404Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:04:09.7070679Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:04:09.7070924Z         
2025-03-14T05:04:09.7071326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7071797Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:04:09.7072069Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:04:09.7072314Z         
2025-03-14T05:04:09.7072706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7073201Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:04:09.7073521Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:04:09.7073787Z         
2025-03-14T05:04:09.7074187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7074673Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:04:09.7074988Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:04:09.7075248Z         
2025-03-14T05:04:09.7075690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7076326Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:04:09.7076634Z         
2025-03-14T05:04:09.7077058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7077621Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:04:09.7077912Z         
2025-03-14T05:04:09.7078382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7079009Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:04:09.7079299Z         
2025-03-14T05:04:09.7079775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7080424Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:04:09.7080744Z         
2025-03-14T05:04:09.7081311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7082469Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:04:09.7082899Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:04:09.7083252Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:04:09.7083519Z         
2025-03-14T05:04:09.7083996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7084601Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:04:09.7084897Z         
2025-03-14T05:04:09.7085309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7085831Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:04:09.7086097Z         
2025-03-14T05:04:09.7086499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7086995Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7087302Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7087627Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:04:09.7087893Z         
2025-03-14T05:04:09.7088307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7088796Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7089092Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7089418Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:04:09.7089719Z         
2025-03-14T05:04:09.7090116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7090601Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7090872Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:04:09.7091149Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:04:09.7091405Z         
2025-03-14T05:04:09.7091814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7092344Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:04:09.7092649Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:04:09.7092932Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:04:09.7093190Z         
2025-03-14T05:04:09.7093594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7094103Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7094417Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:04:09.7094686Z         
2025-03-14T05:04:09.7095102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7095604Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7095951Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:04:09.7096187Z         
2025-03-14T05:04:09.7096575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7097074Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7097388Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:04:09.7097625Z         
2025-03-14T05:04:09.7098018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7098551Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:04:09.7098896Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:04:09.7099128Z         
2025-03-14T05:04:09.7099554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7100085Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:04:09.7100344Z         
2025-03-14T05:04:09.7100762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7101280Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:04:09.7101537Z         
2025-03-14T05:04:09.7101965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7102502Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:04:09.7102817Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:04:09.7103181Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:04:09.7103529Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:04:09.7103796Z         
2025-03-14T05:04:09.7104336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7104914Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:04:09.7105244Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:04:09.7105599Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:04:09.7105944Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:04:09.7106219Z         
2025-03-14T05:04:09.7106668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7107197Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:04:09.7107543Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:04:09.7107940Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:04:09.7108230Z         
2025-03-14T05:04:09.7108672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7109217Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:04:09.7109566Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:04:09.7109938Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:04:09.7110203Z         
2025-03-14T05:04:09.7110622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7111110Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:04:09.7111396Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:04:09.7111645Z         
2025-03-14T05:04:09.7112059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7112543Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:04:09.7112817Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:04:09.7113061Z         
2025-03-14T05:04:09.7113471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7113974Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:04:09.7114274Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:04:09.7114528Z         
2025-03-14T05:04:09.7114924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7115402Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:04:09.7115701Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:04:09.7115954Z         
2025-03-14T05:04:09.7116423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7117013Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:04:09.7117314Z         
2025-03-14T05:04:09.7117733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7118279Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:04:09.7118563Z         
2025-03-14T05:04:09.7119029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7119628Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:04:09.7119919Z         
2025-03-14T05:04:09.7120488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:04:09.7121189Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:04:09.7121466Z         
2025-03-14T05:04:09.7121877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7122378Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:04:09.7122666Z         
2025-03-14T05:04:09.7123186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7123785Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:04:09.7124056Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:04:09.7124326Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:04:09.7124561Z         
2025-03-14T05:04:09.7125105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7125745Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7126165Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:04:09.7126513Z         
2025-03-14T05:04:09.7127052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7127725Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7128007Z         
2025-03-14T05:04:09.7128390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7128862Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:04:09.7129105Z         
2025-03-14T05:04:09.7129615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7130238Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:04:09.7130517Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:04:09.7130797Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:04:09.7131035Z         
2025-03-14T05:04:09.7131573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7132208Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7132625Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:04:09.7132975Z         
2025-03-14T05:04:09.7133510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7134174Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7134457Z         
2025-03-14T05:04:09.7134830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7135344Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:04:09.7135616Z         
2025-03-14T05:04:09.7136149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7136792Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:04:09.7137073Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:04:09.7137356Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:04:09.7137597Z         
2025-03-14T05:04:09.7138133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7138795Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7139236Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:04:09.7139601Z         
2025-03-14T05:04:09.7140138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7140826Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7141119Z         
2025-03-14T05:04:09.7141503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7141990Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:04:09.7142245Z         
2025-03-14T05:04:09.7142779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7143391Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:04:09.7143676Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:04:09.7143998Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:04:09.7144352Z         
2025-03-14T05:04:09.7144931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7145604Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7146047Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:04:09.7146413Z         
2025-03-14T05:04:09.7146971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7147663Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7147957Z         
2025-03-14T05:04:09.7148347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7148835Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:04:09.7149089Z         
2025-03-14T05:04:09.7149672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7150281Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:04:09.7150583Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:04:09.7150866Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:04:09.7151117Z         
2025-03-14T05:04:09.7151668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7152354Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:04:09.7152822Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:04:09.7153184Z         
2025-03-14T05:04:09.7153737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7154412Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7154695Z         
2025-03-14T05:04:09.7155072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7155550Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:04:09.7155800Z         
2025-03-14T05:04:09.7156164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7156881Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:04:09.7157368Z         
2025-03-14T05:04:09.7157734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7158541Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:04:09.7159118Z         
2025-03-14T05:04:09.7159493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7160029Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:04:09.7160347Z         
2025-03-14T05:04:09.7160819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:04:09.7161398Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:04:09.7161663Z         
2025-03-14T05:04:09.7162047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:04:09.7162548Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:04:09.7162817Z         
2025-03-14T05:04:09.7163323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:04:09.7163883Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:04:09.7165228Z         
2025-03-14T05:04:09.7165795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:04:09.7166474Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:04:09.7166787Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:09.7167114Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:04:09.7167452Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:04:09.7167706Z         
2025-03-14T05:04:09.7168160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:04:09.7168698Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:04:09.7168937Z         
2025-03-14T05:04:09.7169031Z 
2025-03-14T05:04:09.7169132Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:09.7171309Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:04:09.7173666Z         l_features_p2_ = L_features_p2_
2025-03-14T05:04:09.7173907Z         l_features_p3_ = L_features_p3_
2025-03-14T05:04:09.7174130Z         l_features_p4_ = L_features_p4_
2025-03-14T05:04:09.7174339Z         l_features_p5_ = L_features_p5_
2025-03-14T05:04:09.7174548Z         l_features_p6_ = L_features_p6_
2025-03-14T05:04:09.7174935Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:04:09.7175487Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:04:09.7176030Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:04:09.7176573Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:04:09.7177137Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:04:09.7177682Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:04:09.7178157Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:04:09.7178700Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:04:09.7179278Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:04:09.7179832Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:04:09.7180372Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:04:09.7180725Z         
2025-03-14T05:04:09.7181259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7182406Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:04:09.7182675Z         
2025-03-14T05:04:09.7183065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7183560Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:04:09.7183818Z         
2025-03-14T05:04:09.7184397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7185095Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:04:09.7185403Z         
2025-03-14T05:04:09.7185837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7186397Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:04:09.7186782Z         
2025-03-14T05:04:09.7187304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.7187963Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:04:09.7188319Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:04:09.7188624Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:04:09.7188892Z         
2025-03-14T05:04:09.7189382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.7189970Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:04:09.7190247Z         
2025-03-14T05:04:09.7190685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.7191225Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:04:09.7191495Z         
2025-03-14T05:04:09.7192033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.7192815Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:04:09.7193210Z         
2025-03-14T05:04:09.7193788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.7194487Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:04:09.7195068Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:04:09.7195623Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:04:09.7195951Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:04:09.7196207Z         
2025-03-14T05:04:09.7196798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7197513Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:04:09.7197784Z         
2025-03-14T05:04:09.7198154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7198636Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:04:09.7198889Z         
2025-03-14T05:04:09.7199396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7200018Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:04:09.7200299Z         
2025-03-14T05:04:09.7200667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7201136Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:04:09.7201389Z         
2025-03-14T05:04:09.7201866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.7202470Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:04:09.7202821Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:04:09.7203109Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:04:09.7203356Z         
2025-03-14T05:04:09.7203772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.7204280Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:04:09.7204538Z         
2025-03-14T05:04:09.7204952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.7205469Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:04:09.7205707Z         
2025-03-14T05:04:09.7206169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.7206920Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:04:09.7207256Z         
2025-03-14T05:04:09.7207742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.7208355Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:04:09.7208839Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:04:09.7209313Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:04:09.7209603Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:04:09.7209831Z         
2025-03-14T05:04:09.7210357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7210962Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:04:09.7211225Z         
2025-03-14T05:04:09.7211595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7212069Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:04:09.7212322Z         
2025-03-14T05:04:09.7212822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7213431Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:04:09.7213692Z         
2025-03-14T05:04:09.7214059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7214530Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:04:09.7214818Z         
2025-03-14T05:04:09.7215277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.7215882Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:04:09.7216231Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:04:09.7216512Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:04:09.7216754Z         
2025-03-14T05:04:09.7217174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.7217680Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:04:09.7217926Z         
2025-03-14T05:04:09.7218334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.7218835Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:04:09.7219076Z         
2025-03-14T05:04:09.7219539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.7220203Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:04:09.7220562Z         
2025-03-14T05:04:09.7221064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.7221666Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:04:09.7222155Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:04:09.7222637Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:04:09.7222935Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:04:09.7223168Z         
2025-03-14T05:04:09.7223687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7224467Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:04:09.7224775Z         
2025-03-14T05:04:09.7225210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7225701Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:04:09.7225963Z         
2025-03-14T05:04:09.7226486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7227121Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:04:09.7227392Z         
2025-03-14T05:04:09.7227770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7228258Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:04:09.7228707Z         
2025-03-14T05:04:09.7229075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.7229276Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:04:09.7229378Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:04:09.7229511Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:04:09.7229576Z         
2025-03-14T05:04:09.7229909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.7230034Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:04:09.7230109Z         
2025-03-14T05:04:09.7230429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.7230560Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:04:09.7230625Z         
2025-03-14T05:04:09.7231016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.7231240Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:04:09.7231332Z         
2025-03-14T05:04:09.7231729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.7231898Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:04:09.7232203Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:04:09.7232323Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:04:09.7232441Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:04:09.7232504Z         
2025-03-14T05:04:09.7232924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7233064Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:04:09.7233135Z         
2025-03-14T05:04:09.7233414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7233554Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:04:09.7233617Z         
2025-03-14T05:04:09.7234037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:04:09.7234177Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:04:09.7234250Z         
2025-03-14T05:04:09.7234532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7234673Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:04:09.7234757Z         
2025-03-14T05:04:09.7235124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:04:09.7235308Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:04:09.7235412Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:04:09.7235528Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:04:09.7235600Z         
2025-03-14T05:04:09.7235916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:04:09.7236044Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:04:09.7236108Z         
2025-03-14T05:04:09.7236431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:04:09.7236550Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:04:09.7236620Z         
2025-03-14T05:04:09.7236983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:04:09.7237227Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:04:09.7237316Z         
2025-03-14T05:04:09.7237713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:04:09.7237860Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:04:09.7238155Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:04:09.7238281Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:04:09.7238393Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:04:09.7238466Z         
2025-03-14T05:04:09.7238759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:04:09.7238889Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:04:09.7239018Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:04:09.7239149Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:04:09.7239271Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:04:09.7239397Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:04:09.7239463Z         
2025-03-14T05:04:09.7239732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.7240159Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:04:09.7240232Z         
2025-03-14T05:04:09.7240519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.7240714Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:04:09.7240806Z         
2025-03-14T05:04:09.7241189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.7241607Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.7241673Z         
2025-03-14T05:04:09.7242044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.7242452Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:04:09.7242527Z         
2025-03-14T05:04:09.7242788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.7243218Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:04:09.7243284Z         
2025-03-14T05:04:09.7243581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.7243785Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:04:09.7243857Z         
2025-03-14T05:04:09.7244228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.7244644Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.7244716Z         
2025-03-14T05:04:09.7245075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.7245480Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:04:09.7245547Z         
2025-03-14T05:04:09.7245808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.7246202Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:04:09.7246272Z         
2025-03-14T05:04:09.7246543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.7246726Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:04:09.7246793Z         
2025-03-14T05:04:09.7247169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.7247580Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.7247655Z         
2025-03-14T05:04:09.7248013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.7248414Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:04:09.7248490Z         
2025-03-14T05:04:09.7248743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.7249142Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:04:09.7249209Z         
2025-03-14T05:04:09.7249488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.7249705Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:04:09.7249778Z         
2025-03-14T05:04:09.7250146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.7250555Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:04:09.7250623Z         
2025-03-14T05:04:09.7250986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.7251371Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:04:09.7251445Z         
2025-03-14T05:04:09.7251695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:04:09.7252268Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:04:09.7252343Z         
2025-03-14T05:04:09.7252609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:04:09.7252784Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:04:09.7252851Z         
2025-03-14T05:04:09.7253228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:04:09.7253847Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:04:09.7253939Z         
2025-03-14T05:04:09.7254295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:04:09.7254888Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:04:09.7254957Z         
2025-03-14T05:04:09.7255305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:04:09.7255470Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:04:09.7255621Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:04:09.7255801Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:04:09.7255983Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:04:09.7256146Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:04:09.7256303Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:04:09.7256455Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:04:09.7256593Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:04:09.7256744Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:04:09.7256876Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:04:09.7256947Z         
2025-03-14T05:04:09.7257381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:04:09.7257559Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:04:09.7257737Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:04:09.7257919Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:04:09.7258077Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:04:09.7258254Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:04:09.7258424Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:04:09.7258574Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:04:09.7258734Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:04:09.7258907Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:04:09.7259064Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:04:09.7259226Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:04:09.7259386Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:04:09.7259539Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:04:09.7259696Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:04:09.7259863Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:04:09.7259928Z         
2025-03-14T05:04:09.7260339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7260553Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:04:09.7260618Z         
2025-03-14T05:04:09.7261077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7261252Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:04:09.7261411Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:04:09.7261591Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:04:09.7261663Z         
2025-03-14T05:04:09.7262036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7262216Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:04:09.7262281Z         
2025-03-14T05:04:09.7262602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7262745Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:04:09.7262817Z         
2025-03-14T05:04:09.7263127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7263266Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7263397Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7263555Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:09.7263621Z         
2025-03-14T05:04:09.7263952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7264085Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7264292Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7264459Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:04:09.7264538Z         
2025-03-14T05:04:09.7264864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7265035Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7265142Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:04:09.7265279Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:04:09.7265344Z         
2025-03-14T05:04:09.7265663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7265815Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:09.7265913Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:04:09.7266043Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:04:09.7266118Z         
2025-03-14T05:04:09.7266438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7266609Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7266726Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:04:09.7266804Z         
2025-03-14T05:04:09.7267102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7267285Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7267430Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:04:09.7267497Z         
2025-03-14T05:04:09.7267817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7267970Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7268090Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:04:09.7268155Z         
2025-03-14T05:04:09.7268471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7268660Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:09.7268782Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:04:09.7268847Z         
2025-03-14T05:04:09.7269201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7269349Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:09.7269424Z         
2025-03-14T05:04:09.7269761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7269909Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:09.7269974Z         
2025-03-14T05:04:09.7270335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7270478Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:09.7270611Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:04:09.7270768Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:09.7270938Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:04:09.7271004Z         
2025-03-14T05:04:09.7271355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7271498Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:09.7271633Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:04:09.7271784Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:09.7271933Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:04:09.7271999Z         
2025-03-14T05:04:09.7272342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7272463Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:09.7272633Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:09.7272766Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:04:09.7272838Z         
2025-03-14T05:04:09.7273211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7273338Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:09.7273507Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:09.7273668Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:04:09.7273734Z         
2025-03-14T05:04:09.7274050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7274155Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:09.7274273Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:09.7274337Z         
2025-03-14T05:04:09.7274645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7274749Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:09.7274866Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:09.7274938Z         
2025-03-14T05:04:09.7275237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7275364Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:09.7275494Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:09.7275567Z         
2025-03-14T05:04:09.7275862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7275982Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:09.7276111Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:09.7276185Z         
2025-03-14T05:04:09.7276523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7276712Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:09.7276796Z         
2025-03-14T05:04:09.7277133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7277296Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:04:09.7277367Z         
2025-03-14T05:04:09.7277745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7277929Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:04:09.7277995Z         
2025-03-14T05:04:09.7278393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7278602Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:04:09.7278672Z         
2025-03-14T05:04:09.7279127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7279312Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:04:09.7279463Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:04:09.7279629Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:04:09.7279693Z         
2025-03-14T05:04:09.7280069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7280238Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:04:09.7280308Z         
2025-03-14T05:04:09.7280614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7280767Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:04:09.7280830Z         
2025-03-14T05:04:09.7281147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7281281Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7282068Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7282268Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:04:09.7282344Z         
2025-03-14T05:04:09.7282661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7282802Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7282938Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7283093Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:04:09.7283168Z         
2025-03-14T05:04:09.7283477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7283666Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7283761Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:04:09.7283902Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:04:09.7283967Z         
2025-03-14T05:04:09.7284283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7284437Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:04:09.7284542Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:04:09.7284675Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:04:09.7284749Z         
2025-03-14T05:04:09.7285051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7285217Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7285333Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:04:09.7285406Z         
2025-03-14T05:04:09.7285736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7285923Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7286040Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:04:09.7286137Z         
2025-03-14T05:04:09.7286436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7286595Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7286708Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:04:09.7286782Z         
2025-03-14T05:04:09.7287081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7287277Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:04:09.7287392Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:04:09.7287467Z         
2025-03-14T05:04:09.7287799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7287949Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:04:09.7288017Z         
2025-03-14T05:04:09.7288352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7288492Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:04:09.7288564Z         
2025-03-14T05:04:09.7288907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7289054Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:04:09.7289193Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:04:09.7289350Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:04:09.7289520Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:04:09.7289587Z         
2025-03-14T05:04:09.7289937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7290079Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:04:09.7290212Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:04:09.7290366Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:04:09.7290513Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:04:09.7290579Z         
2025-03-14T05:04:09.7290912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7291029Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:04:09.7291197Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:04:09.7291334Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:04:09.7291408Z         
2025-03-14T05:04:09.7291774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7291898Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:04:09.7292084Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:04:09.7292228Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:04:09.7292292Z         
2025-03-14T05:04:09.7292613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7292715Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:04:09.7292842Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:04:09.7292910Z         
2025-03-14T05:04:09.7293229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7293331Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:04:09.7293459Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:04:09.7293524Z         
2025-03-14T05:04:09.7293837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7293958Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:04:09.7294105Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:04:09.7294171Z         
2025-03-14T05:04:09.7294490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7294606Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:04:09.7294746Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:04:09.7294811Z         
2025-03-14T05:04:09.7295158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7295372Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:04:09.7295447Z         
2025-03-14T05:04:09.7295783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7295960Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:04:09.7296029Z         
2025-03-14T05:04:09.7296426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7296613Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:04:09.7296680Z         
2025-03-14T05:04:09.7297093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7297304Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:04:09.7297379Z         
2025-03-14T05:04:09.7297858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7298026Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:04:09.7298201Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:04:09.7298352Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:04:09.7298422Z         
2025-03-14T05:04:09.7298812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7298985Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:04:09.7299059Z         
2025-03-14T05:04:09.7299383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7299541Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:04:09.7299609Z         
2025-03-14T05:04:09.7299939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7300075Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7300213Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7300365Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:04:09.7300438Z         
2025-03-14T05:04:09.7300763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7300899Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7301023Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7301186Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:04:09.7301253Z         
2025-03-14T05:04:09.7301577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7301735Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7301856Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:04:09.7301992Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:04:09.7302067Z         
2025-03-14T05:04:09.7302386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7302545Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:04:09.7302643Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:04:09.7302782Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:04:09.7302851Z         
2025-03-14T05:04:09.7303165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7303322Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7303448Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:04:09.7303514Z         
2025-03-14T05:04:09.7303848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7304034Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7304255Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:04:09.7304336Z         
2025-03-14T05:04:09.7304645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7304813Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7304926Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:04:09.7305002Z         
2025-03-14T05:04:09.7305309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7305508Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:04:09.7305621Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:04:09.7305696Z         
2025-03-14T05:04:09.7306034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7306188Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:04:09.7306257Z         
2025-03-14T05:04:09.7306602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7306743Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:04:09.7306822Z         
2025-03-14T05:04:09.7307175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7307315Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:04:09.7307441Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:04:09.7307634Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:04:09.7307775Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:04:09.7307849Z         
2025-03-14T05:04:09.7308187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7308333Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:04:09.7308457Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:04:09.7308615Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:04:09.7308754Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:04:09.7308828Z         
2025-03-14T05:04:09.7309151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7309275Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:04:09.7309435Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:04:09.7309607Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:04:09.7309675Z         
2025-03-14T05:04:09.7310026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7310171Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:04:09.7310336Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:04:09.7310479Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:04:09.7310544Z         
2025-03-14T05:04:09.7310862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7310961Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:04:09.7311090Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:04:09.7311155Z         
2025-03-14T05:04:09.7311473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7311571Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:04:09.7311696Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:04:09.7311765Z         
2025-03-14T05:04:09.7312080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7312198Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:04:09.7312339Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:04:09.7312405Z         
2025-03-14T05:04:09.7312720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7312838Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:04:09.7312976Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:04:09.7313043Z         
2025-03-14T05:04:09.7313404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7313629Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:04:09.7313703Z         
2025-03-14T05:04:09.7314040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7314207Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:04:09.7314272Z         
2025-03-14T05:04:09.7314666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7314844Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:04:09.7314919Z         
2025-03-14T05:04:09.7315323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7315536Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:04:09.7315602Z         
2025-03-14T05:04:09.7316085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7316237Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:04:09.7316414Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:04:09.7316553Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:04:09.7316628Z         
2025-03-14T05:04:09.7316998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7317174Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:04:09.7317239Z         
2025-03-14T05:04:09.7317555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7317708Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:04:09.7317773Z         
2025-03-14T05:04:09.7318088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7318218Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7318350Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7318497Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:04:09.7318570Z         
2025-03-14T05:04:09.7318883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7319015Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7319136Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7319295Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:04:09.7319382Z         
2025-03-14T05:04:09.7319702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7319826Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7319926Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:04:09.7320057Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:04:09.7320133Z         
2025-03-14T05:04:09.7320443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7320598Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:04:09.7320692Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:04:09.7320827Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:04:09.7320893Z         
2025-03-14T05:04:09.7321200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7321353Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7321474Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:04:09.7321559Z         
2025-03-14T05:04:09.7321888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7322036Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7322341Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:04:09.7322407Z         
2025-03-14T05:04:09.7322709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7322855Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7322971Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:04:09.7323036Z         
2025-03-14T05:04:09.7323337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7323518Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:04:09.7323635Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:04:09.7323699Z         
2025-03-14T05:04:09.7324032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7324179Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:04:09.7324244Z         
2025-03-14T05:04:09.7324569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7324705Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:04:09.7324777Z         
2025-03-14T05:04:09.7325106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7325246Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:04:09.7325394Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:04:09.7325551Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:04:09.7325690Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:04:09.7325761Z         
2025-03-14T05:04:09.7326104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7326250Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:04:09.7326373Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:04:09.7326541Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:04:09.7326675Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:04:09.7326747Z         
2025-03-14T05:04:09.7327062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7327183Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:04:09.7327337Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:04:09.7327495Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:04:09.7327578Z         
2025-03-14T05:04:09.7327914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7328048Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:04:09.7328224Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:04:09.7328368Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:04:09.7328442Z         
2025-03-14T05:04:09.7328743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7328848Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:04:09.7328964Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:04:09.7329038Z         
2025-03-14T05:04:09.7329335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7329436Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:04:09.7329550Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:04:09.7329625Z         
2025-03-14T05:04:09.7329922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7330046Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:04:09.7330175Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:04:09.7330249Z         
2025-03-14T05:04:09.7330546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7330665Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:04:09.7330796Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:04:09.7330889Z         
2025-03-14T05:04:09.7331222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7331415Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:04:09.7331478Z         
2025-03-14T05:04:09.7331806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7331973Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:04:09.7332039Z         
2025-03-14T05:04:09.7332417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7332590Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:04:09.7332661Z         
2025-03-14T05:04:09.7333045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:09.7333273Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:04:09.7333338Z         
2025-03-14T05:04:09.7333788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:09.7333951Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:04:09.7334108Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:04:09.7334243Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:04:09.7334316Z         
2025-03-14T05:04:09.7334685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:09.7334856Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:04:09.7334920Z         
2025-03-14T05:04:09.7335241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:09.7335385Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:04:09.7335458Z         
2025-03-14T05:04:09.7335768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:09.7335902Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:04:09.7336024Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7336181Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:04:09.7336244Z         
2025-03-14T05:04:09.7336573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:09.7336694Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:04:09.7336820Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:04:09.7336965Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:04:09.7337054Z         
2025-03-14T05:04:09.7337352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:09.7337476Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:09.7337564Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:04:09.7337700Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:04:09.7337762Z         
2025-03-14T05:04:09.7338077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:09.7338224Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:04:09.7338325Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:04:09.7338452Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:04:09.7338528Z         
2025-03-14T05:04:09.7338826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:09.7338987Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:09.7339127Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:04:09.7339193Z         
2025-03-14T05:04:09.7339511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:09.7339678Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:09.7339799Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:04:09.7339866Z         
2025-03-14T05:04:09.7340171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:09.7340318Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:09.7340436Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:04:09.7340502Z         
2025-03-14T05:04:09.7340812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:09.7340995Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:04:09.7341112Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:04:09.7341178Z         
2025-03-14T05:04:09.7341522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:09.7341659Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:04:09.7341732Z         
2025-03-14T05:04:09.7342065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:09.7342207Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:04:09.7342274Z         
2025-03-14T05:04:09.7342624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:09.7342762Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:04:09.7342920Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:04:09.7343073Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:04:09.7343220Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:04:09.7343286Z         
2025-03-14T05:04:09.7343636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:09.7343771Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:04:09.7343905Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:04:09.7344058Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:04:09.7344279Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:04:09.7344352Z         
2025-03-14T05:04:09.7344695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:09.7344816Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:04:09.7345019Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:04:09.7345190Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:04:09.7345258Z         
2025-03-14T05:04:09.7345598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:09.7345731Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:04:09.7345905Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:04:09.7346034Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:04:09.7346107Z         
2025-03-14T05:04:09.7346415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:09.7346519Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:04:09.7346634Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:04:09.7346704Z         
2025-03-14T05:04:09.7347009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:09.7347113Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:04:09.7347226Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:04:09.7347298Z         
2025-03-14T05:04:09.7347599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:09.7347721Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:04:09.7347853Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:04:09.7347924Z         
2025-03-14T05:04:09.7348224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:09.7348343Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:04:09.7348472Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:04:09.7348568Z         
2025-03-14T05:04:09.7348910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:09.7349104Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:04:09.7349168Z         
2025-03-14T05:04:09.7349516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:09.7349676Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:04:09.7349747Z         
2025-03-14T05:04:09.7350127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:09.7350305Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:04:09.7350371Z         
2025-03-14T05:04:09.7350854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:04:09.7351009Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:04:09.7351086Z         
2025-03-14T05:04:09.7351399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7351552Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:04:09.7351639Z         
2025-03-14T05:04:09.7352079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7352199Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:04:09.7352312Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:04:09.7352426Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:04:09.7352501Z         
2025-03-14T05:04:09.7352967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7353107Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7353348Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:04:09.7353413Z         
2025-03-14T05:04:09.7353876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7354045Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7354118Z         
2025-03-14T05:04:09.7354423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7354553Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:04:09.7354619Z         
2025-03-14T05:04:09.7355056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7355193Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:04:09.7355306Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:04:09.7355424Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:04:09.7355494Z         
2025-03-14T05:04:09.7355937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7356074Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7356302Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:04:09.7356377Z         
2025-03-14T05:04:09.7356813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7356983Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7357048Z         
2025-03-14T05:04:09.7357363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7357504Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:04:09.7357578Z         
2025-03-14T05:04:09.7357989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7358128Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:04:09.7358231Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:04:09.7358355Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:04:09.7358422Z         
2025-03-14T05:04:09.7358872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7359003Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7359241Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:04:09.7359306Z         
2025-03-14T05:04:09.7359751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7359919Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7359982Z         
2025-03-14T05:04:09.7360274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7360395Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:04:09.7360467Z         
2025-03-14T05:04:09.7360879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7360998Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:04:09.7361120Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:04:09.7361250Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:04:09.7361313Z         
2025-03-14T05:04:09.7361756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7361886Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:09.7362122Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:04:09.7362186Z         
2025-03-14T05:04:09.7362624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7362783Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7362856Z         
2025-03-14T05:04:09.7363138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7363265Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:04:09.7363346Z         
2025-03-14T05:04:09.7363785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:09.7363912Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:04:09.7364021Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:04:09.7364135Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:04:09.7364207Z         
2025-03-14T05:04:09.7364635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:09.7364802Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:04:09.7365027Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:04:09.7365100Z         
2025-03-14T05:04:09.7365536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:09.7365696Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:09.7365769Z         
2025-03-14T05:04:09.7366052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:09.7366178Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:04:09.7366242Z         
2025-03-14T05:04:09.7366518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7366879Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:04:09.7366954Z         
2025-03-14T05:04:09.7367244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7367693Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:04:09.7367758Z         
2025-03-14T05:04:09.7368031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:09.7368224Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:04:09.7368297Z         
2025-03-14T05:04:09.7368666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:04:09.7368813Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:04:09.7368875Z         
2025-03-14T05:04:09.7369171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:04:09.7369339Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:04:09.7369414Z         
2025-03-14T05:04:09.7369796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:04:09.7369956Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:04:09.7370020Z         
2025-03-14T05:04:09.7370490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:04:09.7370632Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:04:09.7370749Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:09.7370909Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:04:09.7371036Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:04:09.7371107Z         
2025-03-14T05:04:09.7371459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:04:09.7371582Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:04:09.7371646Z         
2025-03-14T05:04:10.9228890Z 
2025-03-14T05:04:10.9235498Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:10.9238333Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T05:04:10.9240094Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:04:10.9242172Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:04:10.9247855Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T05:04:10.9251589Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T05:04:10.9256162Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T05:04:10.9258401Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T05:04:10.9258819Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T05:04:10.9262102Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T05:04:10.9266839Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T05:04:10.9267243Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T05:04:10.9271910Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:04:10.9276878Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T05:04:10.9277332Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T05:04:10.9283389Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T05:04:10.9287170Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T05:04:10.9291060Z         
2025-03-14T05:04:10.9295177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:10.9296585Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:04:10.9296949Z         
2025-03-14T05:04:10.9303271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:10.9304493Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:04:10.9305507Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:04:10.9306007Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:04:10.9310845Z         
2025-03-14T05:04:10.9315940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:10.9318576Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:04:10.9322801Z         
2025-03-14T05:04:10.9325271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:10.9326715Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:04:10.9327949Z         
2025-03-14T05:04:10.9331242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:10.9331766Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:10.9332087Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9332431Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:04:10.9332713Z         
2025-03-14T05:04:10.9333123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:10.9333621Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:10.9333926Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:10.9334446Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:04:10.9334730Z         
2025-03-14T05:04:10.9336439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:10.9336944Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9337222Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:04:10.9337494Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:04:10.9337739Z         
2025-03-14T05:04:10.9338140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:10.9338663Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:10.9338960Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:04:10.9339234Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:04:10.9339483Z         
2025-03-14T05:04:10.9339898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:10.9340457Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:10.9340825Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:04:10.9341070Z         
2025-03-14T05:04:10.9341464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:10.9342008Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:10.9342341Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:04:10.9342578Z         
2025-03-14T05:04:10.9342968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:10.9343473Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:10.9343799Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:04:10.9344044Z         
2025-03-14T05:04:10.9344570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:10.9345126Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:10.9345491Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:04:10.9345733Z         
2025-03-14T05:04:10.9346161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:10.9346689Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:10.9346951Z         
2025-03-14T05:04:10.9347369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:10.9347892Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:10.9348150Z         
2025-03-14T05:04:10.9348588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:10.9349143Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:10.9349459Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:04:10.9349790Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:10.9350136Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:04:10.9350394Z         
2025-03-14T05:04:10.9350806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:10.9351340Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:10.9351648Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:04:10.9351978Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:10.9352315Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:04:10.9352571Z         
2025-03-14T05:04:10.9352976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:10.9353494Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:10.9353842Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:10.9354186Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:04:10.9354462Z         
2025-03-14T05:04:10.9354884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:10.9355392Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:10.9355724Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:10.9356072Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:04:10.9356328Z         
2025-03-14T05:04:10.9356723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:10.9357181Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:10.9357447Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:10.9357684Z         
2025-03-14T05:04:10.9358097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:10.9358547Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:10.9358812Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:10.9359059Z         
2025-03-14T05:04:10.9359440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:10.9359908Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:10.9360204Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:10.9360451Z         
2025-03-14T05:04:10.9360837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:10.9361302Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:10.9361616Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:10.9361866Z         
2025-03-14T05:04:10.9362289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:10.9362857Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:10.9363148Z         
2025-03-14T05:04:10.9363559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:10.9364099Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:04:10.9364380Z         
2025-03-14T05:04:10.9364839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:10.9365435Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:04:10.9365729Z         
2025-03-14T05:04:10.9366192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:10.9366870Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T05:04:10.9367212Z         
2025-03-14T05:04:10.9367717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:10.9368438Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T05:04:10.9368830Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:04:10.9369170Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:04:10.9369423Z         
2025-03-14T05:04:10.9369879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:10.9370469Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:04:10.9370754Z         
2025-03-14T05:04:10.9371147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:10.9371654Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:04:10.9371922Z         
2025-03-14T05:04:10.9372318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:10.9372813Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:04:10.9373124Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9373455Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T05:04:10.9373723Z         
2025-03-14T05:04:10.9374124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:10.9374612Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:04:10.9374915Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:04:10.9375261Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T05:04:10.9375528Z         
2025-03-14T05:04:10.9375915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:10.9376389Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9376657Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:04:10.9376933Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T05:04:10.9377184Z         
2025-03-14T05:04:10.9377572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:10.9378075Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:04:10.9378371Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:04:10.9378644Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T05:04:10.9378894Z         
2025-03-14T05:04:10.9379280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:10.9379806Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:10.9380156Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T05:04:10.9380398Z         
2025-03-14T05:04:10.9380786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:10.9381316Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:10.9382351Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T05:04:10.9382599Z         
2025-03-14T05:04:10.9382997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:10.9383506Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:10.9383842Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T05:04:10.9384084Z         
2025-03-14T05:04:10.9384550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:10.9385101Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:04:10.9385457Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T05:04:10.9385698Z         
2025-03-14T05:04:10.9386135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:10.9386681Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:04:10.9386950Z         
2025-03-14T05:04:10.9387394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:10.9387932Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:04:10.9388203Z         
2025-03-14T05:04:10.9388639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:10.9389295Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:04:10.9389615Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T05:04:10.9389962Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:04:10.9390321Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T05:04:10.9390593Z         
2025-03-14T05:04:10.9391027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:10.9391574Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:04:10.9391896Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T05:04:10.9392232Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:04:10.9392582Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T05:04:10.9392844Z         
2025-03-14T05:04:10.9393262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:10.9393807Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:04:10.9394176Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:04:10.9394537Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T05:04:10.9394826Z         
2025-03-14T05:04:10.9395251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:10.9395756Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:04:10.9396097Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:04:10.9396461Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T05:04:10.9396724Z         
2025-03-14T05:04:10.9397133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:10.9397609Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:04:10.9397889Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:04:10.9398135Z         
2025-03-14T05:04:10.9398536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:10.9399004Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:04:10.9399278Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:04:10.9399527Z         
2025-03-14T05:04:10.9399927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:10.9400421Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:04:10.9400749Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:04:10.9401009Z         
2025-03-14T05:04:10.9401415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:10.9401915Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:04:10.9402220Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:04:10.9402475Z         
2025-03-14T05:04:10.9402907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:10.9403509Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:04:10.9403813Z         
2025-03-14T05:04:10.9404228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:10.9404766Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:04:10.9405053Z         
2025-03-14T05:04:10.9405516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:10.9406113Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:04:10.9406407Z         
2025-03-14T05:04:10.9406905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:10.9407580Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T05:04:10.9407927Z         
2025-03-14T05:04:10.9408433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:10.9409092Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T05:04:10.9409476Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:04:10.9409815Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:04:10.9410060Z         
2025-03-14T05:04:10.9410532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:10.9411121Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T05:04:10.9411411Z         
2025-03-14T05:04:10.9411800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:10.9412304Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:04:10.9412571Z         
2025-03-14T05:04:10.9412965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:10.9413454Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:04:10.9413766Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9414098Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T05:04:10.9414365Z         
2025-03-14T05:04:10.9414772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:10.9415292Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:04:10.9415589Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:04:10.9415912Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T05:04:10.9416181Z         
2025-03-14T05:04:10.9416572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:10.9417051Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9417322Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:04:10.9417593Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T05:04:10.9417846Z         
2025-03-14T05:04:10.9418240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:10.9418747Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:04:10.9419043Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:04:10.9419321Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T05:04:10.9419573Z         
2025-03-14T05:04:10.9420003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:10.9420554Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:10.9420892Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T05:04:10.9421155Z         
2025-03-14T05:04:10.9421551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:10.9422056Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:10.9422383Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T05:04:10.9422628Z         
2025-03-14T05:04:10.9423019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:10.9423524Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:10.9423846Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T05:04:10.9424087Z         
2025-03-14T05:04:10.9424574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:10.9425162Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:04:10.9425520Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T05:04:10.9425767Z         
2025-03-14T05:04:10.9426189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:10.9426725Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:04:10.9426993Z         
2025-03-14T05:04:10.9427418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:10.9427946Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:04:10.9428214Z         
2025-03-14T05:04:10.9428679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:10.9429225Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:04:10.9429542Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T05:04:10.9429887Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:04:10.9430242Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T05:04:10.9430506Z         
2025-03-14T05:04:10.9430948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:10.9431495Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:04:10.9431818Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T05:04:10.9432158Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:04:10.9432506Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T05:04:10.9432763Z         
2025-03-14T05:04:10.9433369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:10.9433896Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:04:10.9434238Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:04:10.9434618Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T05:04:10.9434881Z         
2025-03-14T05:04:10.9435299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:10.9435801Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:04:10.9436140Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:04:10.9436505Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T05:04:10.9436768Z         
2025-03-14T05:04:10.9437170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:10.9437642Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:04:10.9437918Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:04:10.9438162Z         
2025-03-14T05:04:10.9438562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:10.9439025Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:04:10.9439295Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:04:10.9439543Z         
2025-03-14T05:04:10.9439939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:10.9440424Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:04:10.9440734Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:04:10.9440990Z         
2025-03-14T05:04:10.9441384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:10.9441898Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:04:10.9442209Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:04:10.9442467Z         
2025-03-14T05:04:10.9442906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:10.9443506Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:04:10.9443823Z         
2025-03-14T05:04:10.9444228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:10.9444775Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:04:10.9445059Z         
2025-03-14T05:04:10.9445516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:10.9446105Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:04:10.9446396Z         
2025-03-14T05:04:10.9446906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:10.9447562Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T05:04:10.9447917Z         
2025-03-14T05:04:10.9448423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:10.9449084Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T05:04:10.9449470Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:04:10.9449813Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:04:10.9450096Z         
2025-03-14T05:04:10.9450564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:10.9451162Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:04:10.9451455Z         
2025-03-14T05:04:10.9451864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:10.9452373Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:04:10.9452641Z         
2025-03-14T05:04:10.9453036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:10.9453533Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:04:10.9453839Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9454170Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T05:04:10.9454440Z         
2025-03-14T05:04:10.9454844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:10.9455375Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:04:10.9455680Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:04:10.9456007Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T05:04:10.9456280Z         
2025-03-14T05:04:10.9456682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:10.9457175Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9457455Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:04:10.9457733Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T05:04:10.9457987Z         
2025-03-14T05:04:10.9458393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:10.9458911Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:04:10.9459212Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:04:10.9459487Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T05:04:10.9459745Z         
2025-03-14T05:04:10.9460183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:10.9460701Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:10.9461051Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T05:04:10.9461295Z         
2025-03-14T05:04:10.9461685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:10.9462195Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:10.9462524Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T05:04:10.9462765Z         
2025-03-14T05:04:10.9463158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:10.9463660Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:10.9463981Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T05:04:10.9464300Z         
2025-03-14T05:04:10.9464699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:10.9465241Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:04:10.9465590Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T05:04:10.9465828Z         
2025-03-14T05:04:10.9466250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:10.9466778Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:04:10.9467040Z         
2025-03-14T05:04:10.9467453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:10.9467974Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:04:10.9468275Z         
2025-03-14T05:04:10.9468699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:10.9469237Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:04:10.9469548Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T05:04:10.9469888Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:04:10.9470246Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T05:04:10.9470516Z         
2025-03-14T05:04:10.9470950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:10.9471494Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:04:10.9471815Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T05:04:10.9472154Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:04:10.9472510Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T05:04:10.9473830Z         
2025-03-14T05:04:10.9474288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:10.9474792Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:04:10.9475146Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:04:10.9475496Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T05:04:10.9475755Z         
2025-03-14T05:04:10.9476171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:10.9476669Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:04:10.9477009Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:04:10.9477371Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T05:04:10.9477628Z         
2025-03-14T05:04:10.9478016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:10.9478470Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:04:10.9478738Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:04:10.9478972Z         
2025-03-14T05:04:10.9479361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:10.9479808Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:04:10.9480068Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:04:10.9480303Z         
2025-03-14T05:04:10.9480689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:10.9481156Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:04:10.9482174Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:04:10.9482527Z         
2025-03-14T05:04:10.9483039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:10.9483512Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:04:10.9483810Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:04:10.9484055Z         
2025-03-14T05:04:10.9484480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:10.9485066Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:04:10.9485366Z         
2025-03-14T05:04:10.9485774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:10.9486312Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:04:10.9486588Z         
2025-03-14T05:04:10.9487046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:10.9487678Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:04:10.9487967Z         
2025-03-14T05:04:10.9488465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:04:10.9489166Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T05:04:10.9489491Z         
2025-03-14T05:04:10.9489988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:04:10.9490670Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T05:04:10.9491051Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:04:10.9491388Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:04:10.9491639Z         
2025-03-14T05:04:10.9492084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:10.9492661Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T05:04:10.9492947Z         
2025-03-14T05:04:10.9493336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:10.9493850Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:04:10.9494108Z         
2025-03-14T05:04:10.9494499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:10.9494984Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:04:10.9495282Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9495604Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T05:04:10.9495864Z         
2025-03-14T05:04:10.9496291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:10.9496772Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:04:10.9497068Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:04:10.9497390Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T05:04:10.9497658Z         
2025-03-14T05:04:10.9498053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:10.9498529Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:04:10.9498798Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:04:10.9499068Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T05:04:10.9499317Z         
2025-03-14T05:04:10.9499706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:10.9500206Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:04:10.9500497Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:04:10.9500787Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T05:04:10.9501036Z         
2025-03-14T05:04:10.9501463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:10.9501987Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:10.9502314Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T05:04:10.9502549Z         
2025-03-14T05:04:10.9502934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:10.9503438Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:10.9503761Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T05:04:10.9503998Z         
2025-03-14T05:04:10.9504483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:10.9504996Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:10.9505322Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T05:04:10.9505563Z         
2025-03-14T05:04:10.9505962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:10.9506511Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:04:10.9506862Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T05:04:10.9507096Z         
2025-03-14T05:04:10.9507521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:10.9508053Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:04:10.9508318Z         
2025-03-14T05:04:10.9508731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:10.9509293Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:04:10.9509548Z         
2025-03-14T05:04:10.9509975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:10.9510506Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:04:10.9510827Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T05:04:10.9511163Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:04:10.9511512Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T05:04:10.9511775Z         
2025-03-14T05:04:10.9512214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:10.9512751Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:04:10.9513070Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T05:04:10.9513396Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:04:10.9513761Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T05:04:10.9514023Z         
2025-03-14T05:04:10.9514466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:10.9514980Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:04:10.9515308Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:04:10.9515661Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T05:04:10.9515921Z         
2025-03-14T05:04:10.9516343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:10.9516848Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:04:10.9517181Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:04:10.9517538Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T05:04:10.9517793Z         
2025-03-14T05:04:10.9518193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:10.9518662Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:04:10.9518934Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:04:10.9519174Z         
2025-03-14T05:04:10.9519569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:10.9520041Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:04:10.9520309Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:04:10.9520548Z         
2025-03-14T05:04:10.9520945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:10.9521431Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:04:10.9521741Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:04:10.9522044Z         
2025-03-14T05:04:10.9522438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:10.9522916Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:04:10.9523221Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:04:10.9523472Z         
2025-03-14T05:04:10.9523913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:10.9524508Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:04:10.9524814Z         
2025-03-14T05:04:10.9525235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:10.9525782Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:04:10.9526066Z         
2025-03-14T05:04:10.9526525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:04:10.9527163Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:04:10.9527449Z         
2025-03-14T05:04:10.9528043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:04:10.9528765Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:04:10.9529019Z         
2025-03-14T05:04:10.9529407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9529895Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:04:10.9530152Z         
2025-03-14T05:04:10.9542975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:10.9543813Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:04:10.9544255Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:04:10.9544561Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:04:10.9544807Z         
2025-03-14T05:04:10.9545390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:10.9546059Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:10.9546484Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T05:04:10.9546833Z         
2025-03-14T05:04:10.9547393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:10.9548074Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:10.9548361Z         
2025-03-14T05:04:10.9548874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9549365Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:04:10.9549615Z         
2025-03-14T05:04:10.9550153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:10.9550818Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T05:04:10.9551168Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:04:10.9551461Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:04:10.9551710Z         
2025-03-14T05:04:10.9552260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:10.9552913Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:10.9553348Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T05:04:10.9553708Z         
2025-03-14T05:04:10.9554292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:10.9554942Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:10.9555255Z         
2025-03-14T05:04:10.9555632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9556097Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:04:10.9556340Z         
2025-03-14T05:04:10.9556835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:10.9557480Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T05:04:10.9557808Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:04:10.9558074Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:04:10.9558310Z         
2025-03-14T05:04:10.9558837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:10.9559457Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:10.9559869Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T05:04:10.9560210Z         
2025-03-14T05:04:10.9560728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:10.9561375Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:10.9561650Z         
2025-03-14T05:04:10.9562018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9562507Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:04:10.9562749Z         
2025-03-14T05:04:10.9563251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:10.9563887Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T05:04:10.9564217Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:04:10.9564490Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:04:10.9564723Z         
2025-03-14T05:04:10.9565250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:10.9565870Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:04:10.9566285Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T05:04:10.9566626Z         
2025-03-14T05:04:10.9567165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:10.9567836Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:10.9568138Z         
2025-03-14T05:04:10.9568518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9568990Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:04:10.9569235Z         
2025-03-14T05:04:10.9569752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:04:10.9570398Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T05:04:10.9570739Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:04:10.9571021Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:04:10.9571259Z         
2025-03-14T05:04:10.9571798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:04:10.9572469Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:04:10.9572918Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T05:04:10.9573271Z         
2025-03-14T05:04:10.9573806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:04:10.9574479Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:04:10.9574766Z         
2025-03-14T05:04:10.9575145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:04:10.9575645Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:04:10.9575897Z         
2025-03-14T05:04:10.9576268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:10.9576981Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:04:10.9577459Z         
2025-03-14T05:04:10.9577822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:10.9578603Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:04:10.9579171Z         
2025-03-14T05:04:10.9579530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:10.9580055Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T05:04:10.9580363Z         
2025-03-14T05:04:10.9580881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:04:10.9582179Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:04:10.9582601Z         
2025-03-14T05:04:10.9582988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:04:10.9583493Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T05:04:10.9583759Z         
2025-03-14T05:04:10.9584344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:04:10.9584916Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:04:10.9585169Z         
2025-03-14T05:04:10.9585745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:04:10.9586400Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:04:10.9586693Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:10.9587021Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:04:10.9587365Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:04:10.9587623Z         
2025-03-14T05:04:10.9588079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:04:10.9588616Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:04:10.9588854Z         
2025-03-14T05:04:17.3527043Z 
2025-03-14T05:04:17.3532795Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.3536389Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.3538857Z         l_stack0_ = L_stack0_
2025-03-14T05:04:17.3539235Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:04:17.3539752Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:04:17.3540262Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:04:17.3540740Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:04:17.3541341Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:04:17.3541967Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:04:17.3542547Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:04:17.3543191Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:04:17.3543698Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3544245Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3544680Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3545096Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3545416Z         
2025-03-14T05:04:17.3545834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:04:17.3546342Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:04:17.3547060Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:04:17.3547796Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:04:17.3548532Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:04:17.3549346Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:04:17.3549640Z         
2025-03-14T05:04:17.3550062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:04:17.3551054Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:04:17.3551758Z         
2025-03-14T05:04:17.3552189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:04:17.3553158Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:04:17.3553875Z         
2025-03-14T05:04:17.3554251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3554708Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3554959Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.3555215Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.3555504Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3555753Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.3556010Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.3556278Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3556574Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.3556805Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.3557067Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3557311Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.3557536Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.3557753Z         
2025-03-14T05:04:17.3558121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.3558870Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.3559413Z         
2025-03-14T05:04:17.3559879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.3560439Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:04:17.3560712Z         
2025-03-14T05:04:17.3561100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.3561623Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.3561899Z         
2025-03-14T05:04:17.3562290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.3562782Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.3563092Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3563418Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.3563698Z         
2025-03-14T05:04:17.3564096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.3564581Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.3564884Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.3565217Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.3565485Z         
2025-03-14T05:04:17.3565869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.3566347Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3566625Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.3566901Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.3567145Z         
2025-03-14T05:04:17.3567537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.3568036Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.3568355Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.3568657Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.3568910Z         
2025-03-14T05:04:17.3569314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.3569828Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.3570145Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.3570377Z         
2025-03-14T05:04:17.3570756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.3571245Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.3571562Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.3571795Z         
2025-03-14T05:04:17.3572175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.3572663Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.3572981Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.3573212Z         
2025-03-14T05:04:17.3573592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.3574114Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.3574448Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.3574677Z         
2025-03-14T05:04:17.3575098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.3575625Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.3575882Z         
2025-03-14T05:04:17.3576298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.3576829Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.3577083Z         
2025-03-14T05:04:17.3577512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.3578055Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.3578381Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.3578716Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.3579062Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.3579322Z         
2025-03-14T05:04:17.3579755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.3580299Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.3580619Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.3580964Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.3581320Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.3583058Z         
2025-03-14T05:04:17.3583670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.3584269Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.3584611Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.3584973Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.3585238Z         
2025-03-14T05:04:17.3585681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.3586213Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.3586576Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.3586932Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.3587187Z         
2025-03-14T05:04:17.3587592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.3588071Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.3588341Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.3588581Z         
2025-03-14T05:04:17.3588985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.3589459Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.3589728Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.3589965Z         
2025-03-14T05:04:17.3590368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.3590910Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.3591200Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.3591447Z         
2025-03-14T05:04:17.3591832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.3592297Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.3592586Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.3592829Z         
2025-03-14T05:04:17.3593257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.3593843Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.3594137Z         
2025-03-14T05:04:17.3594556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.3595117Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.3595405Z         
2025-03-14T05:04:17.3595885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.3596597Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.3597039Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.3597328Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.3597628Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.3597935Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.3598188Z         
2025-03-14T05:04:17.3598567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3599122Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3600981Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.3601247Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.3601621Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3601971Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.3602218Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.3602585Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3602927Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.3603896Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.3604515Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3604862Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.3605100Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.3605947Z         
2025-03-14T05:04:17.3606445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.3607034Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:04:17.3607382Z         
2025-03-14T05:04:17.3607866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.3608537Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.3608953Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.3609242Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.3609541Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.3609844Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.3610099Z         
2025-03-14T05:04:17.3610654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.3611356Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.3611692Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.3612048Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.3612406Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.3612706Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.3612942Z         
2025-03-14T05:04:17.3613369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.3613900Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.3614128Z         
2025-03-14T05:04:17.3614254Z 
2025-03-14T05:04:17.3614350Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.3616187Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.3618156Z         l_stack0_ = L_stack0_
2025-03-14T05:04:17.3618498Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:04:17.3618971Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:04:17.3619438Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:04:17.3620066Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:04:17.3620580Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:04:17.3621158Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:04:17.3621712Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:04:17.3622265Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:04:17.3622739Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3623143Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3623541Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3623941Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3624327Z         
2025-03-14T05:04:17.3624727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:04:17.3625245Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:04:17.3626030Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:04:17.3626752Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:04:17.3627551Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:04:17.3628258Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:04:17.3628543Z         
2025-03-14T05:04:17.3628948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:04:17.3629892Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:04:17.3630601Z         
2025-03-14T05:04:17.3631014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:04:17.3632002Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:04:17.3632727Z         
2025-03-14T05:04:17.3633111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3633579Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3633841Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.3634082Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.3634361Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3634635Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.3634877Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.3635151Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3635396Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.3635634Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.3635900Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3636151Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.3636383Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.3636599Z         
2025-03-14T05:04:17.3636974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.3637746Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.3638297Z         
2025-03-14T05:04:17.3638753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.3639329Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:04:17.3639621Z         
2025-03-14T05:04:17.3640042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.3640573Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.3640876Z         
2025-03-14T05:04:17.3641278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.3641787Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.3642107Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3642439Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.3642707Z         
2025-03-14T05:04:17.3643116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.3643617Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.3643931Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.3644268Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.3644543Z         
2025-03-14T05:04:17.3644938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.3645429Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3645709Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.3645981Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.3646228Z         
2025-03-14T05:04:17.3646627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.3647147Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.3647452Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.3647906Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.3648163Z         
2025-03-14T05:04:17.3648583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.3649108Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.3649443Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.3649686Z         
2025-03-14T05:04:17.3650079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.3650600Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.3650931Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.3651167Z         
2025-03-14T05:04:17.3651556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.3652060Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.3652385Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.3652640Z         
2025-03-14T05:04:17.3653058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.3653599Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.3653964Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.3654196Z         
2025-03-14T05:04:17.3654626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.3655159Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.3655410Z         
2025-03-14T05:04:17.3655826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.3656352Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.3656606Z         
2025-03-14T05:04:17.3657040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.3657581Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.3657909Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.3658247Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.3658595Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.3658855Z         
2025-03-14T05:04:17.3659294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.3659840Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.3660180Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.3660527Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.3660912Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.3661181Z         
2025-03-14T05:04:17.3661625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.3662151Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.3662502Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.3662866Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.3663133Z         
2025-03-14T05:04:17.3663582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.3664200Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.3664572Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.3664944Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.3665211Z         
2025-03-14T05:04:17.3665660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.3666150Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.3666413Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.3666637Z         
2025-03-14T05:04:17.3667048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.3667498Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.3667758Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.3667985Z         
2025-03-14T05:04:17.3668376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.3668844Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.3669138Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.3669384Z         
2025-03-14T05:04:17.3669770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.3670244Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.3670542Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.3670786Z         
2025-03-14T05:04:17.3671215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.3671786Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.3672073Z         
2025-03-14T05:04:17.3672491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.3673049Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.3673352Z         
2025-03-14T05:04:17.3673792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.3674479Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.3674897Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.3675181Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.3675474Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.3676788Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.3677045Z         
2025-03-14T05:04:17.3677426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3677994Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3678340Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.3678587Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.3678951Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3679295Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.3679534Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.3680331Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3680730Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.3680970Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.3681326Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3682532Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.3682830Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.3683071Z         
2025-03-14T05:04:17.3683517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.3684092Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:04:17.3684387Z         
2025-03-14T05:04:17.3684843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.3685525Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.3685948Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.3686238Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.3686538Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.3686843Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.3687095Z         
2025-03-14T05:04:17.3687646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.3688336Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.3688670Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.3688992Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.3689317Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.3689678Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.3689910Z         
2025-03-14T05:04:17.3690339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.3690851Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.3691087Z         
2025-03-14T05:04:17.3691231Z 
2025-03-14T05:04:17.3691332Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.3693220Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.3695171Z         l_stack0_ = L_stack0_
2025-03-14T05:04:17.3695514Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:04:17.3696018Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:04:17.3696484Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:04:17.3696943Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:04:17.3697455Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:04:17.3698010Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:04:17.3698574Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:04:17.3699127Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:04:17.3699602Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3700003Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3700393Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3700782Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3701071Z         
2025-03-14T05:04:17.3701440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:04:17.3701908Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:04:17.3702603Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:04:17.3703342Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:04:17.3704165Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:04:17.3704969Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:04:17.3705280Z         
2025-03-14T05:04:17.3705708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:04:17.3706729Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:04:17.3707521Z         
2025-03-14T05:04:17.3707982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:04:17.3709126Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:04:17.3709965Z         
2025-03-14T05:04:17.3710381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3710894Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3711176Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.3711436Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.3711738Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3712015Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.3712262Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.3712530Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3712771Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.3712999Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.3713267Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3713508Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.3713736Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.3713950Z         
2025-03-14T05:04:17.3714312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.3715059Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.3715589Z         
2025-03-14T05:04:17.3716037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.3716596Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:04:17.3716859Z         
2025-03-14T05:04:17.3717269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.3717781Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.3718056Z         
2025-03-14T05:04:17.3718442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.3718928Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.3719238Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3719556Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.3719818Z         
2025-03-14T05:04:17.3720211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.3720699Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.3721005Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.3721333Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.3721597Z         
2025-03-14T05:04:17.3722014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.3722496Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3722796Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.3723069Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.3723318Z         
2025-03-14T05:04:17.3723706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.3724211Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.3724511Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.3724791Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.3725040Z         
2025-03-14T05:04:17.3725439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.3725938Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.3726257Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.3726488Z         
2025-03-14T05:04:17.3726863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.3727350Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.3727666Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.3727898Z         
2025-03-14T05:04:17.3728278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.3728777Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.3729101Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.3729338Z         
2025-03-14T05:04:17.3729756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.3730276Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.3730619Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.3730847Z         
2025-03-14T05:04:17.3731268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.3731790Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.3732042Z         
2025-03-14T05:04:17.3732454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.3732963Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.3733211Z         
2025-03-14T05:04:17.3733631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.3734166Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.3734520Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.3734864Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.3735206Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.3735481Z         
2025-03-14T05:04:17.3735907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.3736440Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.3736753Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.3737074Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.3737408Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.3737656Z         
2025-03-14T05:04:17.3738063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.3738550Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.3738871Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.3739203Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.3739448Z         
2025-03-14T05:04:17.3739852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.3740339Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.3740662Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.3741000Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.3741247Z         
2025-03-14T05:04:17.3741625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.3742092Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.3742349Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.3742577Z         
2025-03-14T05:04:17.3742961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.3743402Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.3743655Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.3743893Z         
2025-03-14T05:04:17.3744391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.3744903Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.3745218Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.3745481Z         
2025-03-14T05:04:17.3745900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.3746403Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.3746712Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.3746973Z         
2025-03-14T05:04:17.3747475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.3748089Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.3748424Z         
2025-03-14T05:04:17.3748872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.3749463Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.3749764Z         
2025-03-14T05:04:17.3750240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.3751999Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.3752509Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.3752818Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.3753137Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.3753469Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.3753728Z         
2025-03-14T05:04:17.3754121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3754691Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3755053Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.3755300Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.3755667Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3756654Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.3756924Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.3757304Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3757704Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.3757956Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.3758305Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3758639Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.3758873Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.3759092Z         
2025-03-14T05:04:17.3759534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.3760103Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:04:17.3760689Z         
2025-03-14T05:04:17.3761215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.3761920Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.3762354Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.3762649Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.3762982Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.3763322Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.3763581Z         
2025-03-14T05:04:17.3764148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.3764878Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.3765533Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.3765887Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.3766228Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.3766522Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.3766763Z         
2025-03-14T05:04:17.3767206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.3767729Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.3767968Z         
2025-03-14T05:04:17.3768123Z 
2025-03-14T05:04:17.3768216Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.3770375Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.3772449Z         l_stack0_ = L_stack0_
2025-03-14T05:04:17.3772805Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:04:17.3773627Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:04:17.3774141Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:04:17.3774633Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:04:17.3775177Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:04:17.3775764Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:04:17.3776364Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:04:17.3776951Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:04:17.3777455Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3777904Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3778332Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3778733Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.3779059Z         
2025-03-14T05:04:17.3779447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:04:17.3779935Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:04:17.3780649Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:04:17.3781381Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:04:17.3782271Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:04:17.3782983Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:04:17.3783270Z         
2025-03-14T05:04:17.3783676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:04:17.3784714Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:04:17.3785424Z         
2025-03-14T05:04:17.3785848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:04:17.3786811Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:04:17.3787604Z         
2025-03-14T05:04:17.3787986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3788456Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3788725Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.3788971Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.3789259Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3789522Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.3789768Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.3790049Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3790306Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.3790547Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.3790823Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.3791073Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.3791308Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.3791560Z         
2025-03-14T05:04:17.3791964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.3792736Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.3793316Z         
2025-03-14T05:04:17.3793770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.3794338Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:04:17.3794594Z         
2025-03-14T05:04:17.3794979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.3795494Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.3795769Z         
2025-03-14T05:04:17.3796159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.3796657Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.3796967Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3797285Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.3797543Z         
2025-03-14T05:04:17.3797941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.3798429Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.3798733Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.3799057Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.3799323Z         
2025-03-14T05:04:17.3799711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.3800219Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.3800490Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.3800759Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.3801001Z         
2025-03-14T05:04:17.3801390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.3801894Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.3802195Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.3802476Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.3802725Z         
2025-03-14T05:04:17.3803121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.3803620Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.3803938Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.3804171Z         
2025-03-14T05:04:17.3804620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.3805117Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.3805461Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.3805689Z         
2025-03-14T05:04:17.3806072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.3806559Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.3806870Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.3807099Z         
2025-03-14T05:04:17.3807476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.3807991Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.3808322Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.3808549Z         
2025-03-14T05:04:17.3808963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.3809480Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.3809730Z         
2025-03-14T05:04:17.3810134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.3810644Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.3810890Z         
2025-03-14T05:04:17.3811311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.3811840Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.3812150Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.3812493Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.3812828Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.3813079Z         
2025-03-14T05:04:17.3813494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.3814026Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.3814326Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.3814645Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.3814977Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.3815227Z         
2025-03-14T05:04:17.3815633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.3816116Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.3816437Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.3816787Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.3817052Z         
2025-03-14T05:04:17.3817468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.3817971Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.3818332Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.3818675Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.3818921Z         
2025-03-14T05:04:17.3819304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.3819755Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.3820019Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.3820258Z         
2025-03-14T05:04:17.3820643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.3821090Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.3821346Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.3821574Z         
2025-03-14T05:04:17.3821958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.3822418Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.3822698Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.3822936Z         
2025-03-14T05:04:17.3823318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.3823776Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.3824084Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.3824428Z         
2025-03-14T05:04:17.3824929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.3825550Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.3825881Z         
2025-03-14T05:04:17.3826332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.3826927Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.3827230Z         
2025-03-14T05:04:17.3827707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.3828425Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.3828878Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.3829182Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.3829497Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.3829821Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.3830105Z         
2025-03-14T05:04:17.3830529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.3831114Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3831522Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.3831779Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.3832159Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3832517Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.3832767Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.3833142Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3833499Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.3833744Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.3834110Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.3834473Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.3834706Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.3834927Z         
2025-03-14T05:04:17.3835351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.3835906Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:04:17.3836193Z         
2025-03-14T05:04:17.3836645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.3837303Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.3837716Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.3838006Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.3838298Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.3838623Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.3838876Z         
2025-03-14T05:04:17.3839432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.3840111Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.3840444Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.3840772Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.3841106Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.3841396Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.3841636Z         
2025-03-14T05:04:17.3842070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.3842589Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.3842826Z         
2025-03-14T05:04:17.8286072Z 
2025-03-14T05:04:17.8290847Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.8294485Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.8295438Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:04:17.8297205Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:04:17.8297680Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8298120Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8298534Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8298981Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8299292Z         
2025-03-14T05:04:17.8299748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8300238Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8300523Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.8300768Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.8301069Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8301336Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.8301583Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.8301868Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8302121Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.8302360Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.8302639Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8302897Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.8303137Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.8303364Z         
2025-03-14T05:04:17.8303754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.8304715Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.8305666Z         
2025-03-14T05:04:17.8306178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.8306776Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:04:17.8307056Z         
2025-03-14T05:04:17.8307469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.8308034Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.8308328Z         
2025-03-14T05:04:17.8308740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.8309255Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.8309576Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8309962Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.8310240Z         
2025-03-14T05:04:17.8310675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.8311209Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.8311529Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.8311872Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.8312145Z         
2025-03-14T05:04:17.8312551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.8313045Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8313344Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.8313627Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.8313883Z         
2025-03-14T05:04:17.8314288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.8314809Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.8315130Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.8315414Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.8315673Z         
2025-03-14T05:04:17.8316100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.8316627Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.8316972Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.8317222Z         
2025-03-14T05:04:17.8317632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.8318162Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.8318526Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.8318772Z         
2025-03-14T05:04:17.8319169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.8319692Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.8320025Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.8320273Z         
2025-03-14T05:04:17.8320672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.8321210Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.8321563Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.8321795Z         
2025-03-14T05:04:17.8322219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.8322763Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.8323022Z         
2025-03-14T05:04:17.8323523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.8324059Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.8324335Z         
2025-03-14T05:04:17.8324772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.8325326Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.8325651Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.8326000Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.8326351Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.8326613Z         
2025-03-14T05:04:17.8327052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.8327601Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.8327924Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.8328255Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.8328602Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.8328868Z         
2025-03-14T05:04:17.8329293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.8329798Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.8330137Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.8330490Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.8330746Z         
2025-03-14T05:04:17.8331168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.8331704Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.8332046Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.8332396Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.8332653Z         
2025-03-14T05:04:17.8333056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.8333537Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.8333804Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.8334049Z         
2025-03-14T05:04:17.8334451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.8334920Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.8335176Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.8335412Z         
2025-03-14T05:04:17.8335807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.8336311Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.8336623Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.8336875Z         
2025-03-14T05:04:17.8337268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.8337773Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.8338071Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.8338319Z         
2025-03-14T05:04:17.8338766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.8339366Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.8339658Z         
2025-03-14T05:04:17.8340090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.8340657Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.8340943Z         
2025-03-14T05:04:17.8341392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.8342071Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.8342498Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.8342791Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.8343096Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.8343408Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.8343663Z         
2025-03-14T05:04:17.8344051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8344729Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8345122Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.8345381Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.8345757Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8346113Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.8346372Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.8346742Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8347094Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.8347339Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.8347709Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8348067Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.8348307Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.8348536Z         
2025-03-14T05:04:17.8348967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.8349613Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:04:17.8349944Z         
2025-03-14T05:04:17.8350457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.8351202Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.8351641Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.8351936Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.8352248Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.8352558Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.8352823Z         
2025-03-14T05:04:17.8353404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.8354115Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.8354476Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.8354833Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.8355191Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.8355498Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.8355751Z         
2025-03-14T05:04:17.8356212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.8356766Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.8357015Z         
2025-03-14T05:04:17.8368774Z 
2025-03-14T05:04:17.8370811Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.8371845Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.8374026Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:04:17.8374358Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:04:17.8374760Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8375247Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8378099Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8378494Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8378802Z         
2025-03-14T05:04:17.8379240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8379737Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8380028Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.8380276Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.8380570Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8380833Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.8381078Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.8381695Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8382081Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.8382329Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.8382606Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8382920Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.8383156Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.8383381Z         
2025-03-14T05:04:17.8383769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.8384643Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.8385210Z         
2025-03-14T05:04:17.8385706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.8386289Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:04:17.8386570Z         
2025-03-14T05:04:17.8386974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.8387510Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.8387797Z         
2025-03-14T05:04:17.8388199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.8388708Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.8389031Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8389370Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.8389648Z         
2025-03-14T05:04:17.8390062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.8390622Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.8390942Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.8391289Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.8391573Z         
2025-03-14T05:04:17.8391981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.8392483Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8392776Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.8393055Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.8393309Z         
2025-03-14T05:04:17.8393722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.8394257Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.8394572Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.8394865Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.8395160Z         
2025-03-14T05:04:17.8395702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.8396236Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.8396600Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.8396847Z         
2025-03-14T05:04:17.8397255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.8397786Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.8398122Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.8398369Z         
2025-03-14T05:04:17.8398773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.8399299Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.8399632Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.8399881Z         
2025-03-14T05:04:17.8400284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.8400870Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.8401231Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.8401475Z         
2025-03-14T05:04:17.8401919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.8402469Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.8402740Z         
2025-03-14T05:04:17.8403178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.8403724Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.8404016Z         
2025-03-14T05:04:17.8404445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.8404987Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.8405319Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.8405669Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.8406026Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.8406294Z         
2025-03-14T05:04:17.8406740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.8407298Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.8407628Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.8407966Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.8408345Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.8408610Z         
2025-03-14T05:04:17.8409055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.8409564Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.8409921Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.8410277Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.8410544Z         
2025-03-14T05:04:17.8410965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.8411468Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.8411808Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.8412164Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.8412419Z         
2025-03-14T05:04:17.8412821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.8413288Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.8413554Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.8413793Z         
2025-03-14T05:04:17.8414192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.8414652Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.8414923Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.8415160Z         
2025-03-14T05:04:17.8415552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.8416030Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.8416334Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.8416612Z         
2025-03-14T05:04:17.8416999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.8417487Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.8417780Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.8418032Z         
2025-03-14T05:04:17.8418475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.8419071Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.8419368Z         
2025-03-14T05:04:17.8419797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.8420370Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.8420659Z         
2025-03-14T05:04:17.8421112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.8421840Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.8422283Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.8422577Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.8422895Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.8423199Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.8423454Z         
2025-03-14T05:04:17.8423835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8424533Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8424908Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.8425171Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.8425568Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8425932Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.8426184Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.8426571Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8426928Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.8427172Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.8427545Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8427897Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.8428138Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.8428367Z         
2025-03-14T05:04:17.8428807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.8429431Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:04:17.8429773Z         
2025-03-14T05:04:17.8430239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.8430950Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.8431380Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.8431674Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.8431976Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.8432288Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.8432549Z         
2025-03-14T05:04:17.8433109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.8433831Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.8434181Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.8434560Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.8434901Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.8435203Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.8435446Z         
2025-03-14T05:04:17.8435943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.8436482Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.8436748Z         
2025-03-14T05:04:17.8443684Z 
2025-03-14T05:04:17.8449041Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:17.8453759Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:04:17.8455229Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:04:17.8455555Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:04:17.8455890Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8456304Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8456710Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8457106Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:04:17.8457455Z         
2025-03-14T05:04:17.8457883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8458360Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8458618Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:04:17.8458853Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:04:17.8459139Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8459396Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:04:17.8459635Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:04:17.8459916Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8460164Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:04:17.8460400Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:04:17.8460816Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:04:17.8461069Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:04:17.8461304Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:04:17.8461527Z         
2025-03-14T05:04:17.8461923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:04:17.8462707Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:04:17.8463400Z         
2025-03-14T05:04:17.8463875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:04:17.8464613Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:04:17.8464904Z         
2025-03-14T05:04:17.8465332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:04:17.8465960Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:04:17.8466274Z         
2025-03-14T05:04:17.8466779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:04:17.8467305Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:04:17.8467669Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8468005Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:04:17.8468282Z         
2025-03-14T05:04:17.8468701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:04:17.8469225Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:04:17.8469548Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:04:17.8469897Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:04:17.8470196Z         
2025-03-14T05:04:17.8470600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:04:17.8471108Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:04:17.8471399Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:04:17.8471680Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:04:17.8471930Z         
2025-03-14T05:04:17.8472335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:04:17.8472881Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:04:17.8473189Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:04:17.8473483Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:04:17.8473752Z         
2025-03-14T05:04:17.8474180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:04:17.8474787Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:04:17.8475124Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:04:17.8475375Z         
2025-03-14T05:04:17.8475771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:04:17.8476288Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:04:17.8476616Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:04:17.8476858Z         
2025-03-14T05:04:17.8477255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:04:17.8477763Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:04:17.8478096Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:04:17.8478337Z         
2025-03-14T05:04:17.8478729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:04:17.8479269Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:04:17.8479637Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:04:17.8479871Z         
2025-03-14T05:04:17.8480316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:04:17.8480876Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:04:17.8481147Z         
2025-03-14T05:04:17.8481818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:04:17.8482350Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:04:17.8482613Z         
2025-03-14T05:04:17.8483051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:04:17.8483607Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:04:17.8483941Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:04:17.8484285Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:04:17.8484642Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:04:17.8484907Z         
2025-03-14T05:04:17.8485350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:04:17.8485895Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:04:17.8486223Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:04:17.8486564Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:04:17.8486913Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:04:17.8487182Z         
2025-03-14T05:04:17.8487608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:04:17.8488213Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:04:17.8488549Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:04:17.8488906Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:04:17.8489167Z         
2025-03-14T05:04:17.8489596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:04:17.8490098Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:04:17.8490436Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:04:17.8490789Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:04:17.8491048Z         
2025-03-14T05:04:17.8491450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:04:17.8491910Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:04:17.8492184Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:04:17.8492422Z         
2025-03-14T05:04:17.8492865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:04:17.8493354Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:04:17.8493614Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:04:17.8493892Z         
2025-03-14T05:04:17.8494291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:04:17.8494770Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:04:17.8495065Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:04:17.8495315Z         
2025-03-14T05:04:17.8495716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:04:17.8496197Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:04:17.8496495Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:04:17.8496742Z         
2025-03-14T05:04:17.8497181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:04:17.8497764Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:04:17.8498062Z         
2025-03-14T05:04:17.8498488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:04:17.8499048Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:04:17.8499339Z         
2025-03-14T05:04:17.8499794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:04:17.8500478Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:04:17.8500911Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:04:17.8501200Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:04:17.8501546Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:04:17.8501852Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:04:17.8502105Z         
2025-03-14T05:04:17.8502489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:04:17.8503057Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8503415Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:04:17.8503664Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:04:17.8504035Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8504514Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:04:17.8504805Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:04:17.8505219Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8505587Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:04:17.8505852Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:04:17.8506217Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:04:17.8506610Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:04:17.8506896Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:04:17.8507142Z         
2025-03-14T05:04:17.8507623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:04:17.8508317Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:04:17.8508682Z         
2025-03-14T05:04:17.8509181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:04:17.8509940Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:04:17.8510408Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:04:17.8510729Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:04:17.8511064Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:04:17.8511406Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:04:17.8511698Z         
2025-03-14T05:04:17.8512283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:17.8512979Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:04:17.8513321Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:17.8513659Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:04:17.8513998Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:17.8514290Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:17.8514535Z         
2025-03-14T05:04:17.8514976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:17.8515517Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:17.8515758Z         
2025-03-14T05:04:18.2063665Z 
2025-03-14T05:04:18.2068262Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:18.2069782Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:04:18.2070165Z         l_scores_0_ = L_scores_0_
2025-03-14T05:04:18.2070397Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:04:18.2070653Z         
2025-03-14T05:04:18.2071350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:18.2072134Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:04:18.2072488Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:18.2072827Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:04:18.2073159Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:18.2073466Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:18.2073720Z         
2025-03-14T05:04:18.2074576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:18.2075230Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:18.2075499Z         
2025-03-14T05:04:18.2075605Z 
2025-03-14T05:04:18.2075718Z class GraphModule(torch.nn.Module):
2025-03-14T05:04:18.2076139Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:04:18.2076452Z         l_scores_0_ = L_scores_0_
2025-03-14T05:04:18.2076654Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:04:18.2076846Z         
2025-03-14T05:04:18.2077382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:04:18.2078038Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:04:18.2078354Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:04:18.2078670Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:04:18.2078972Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:04:18.2079253Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:04:18.2079486Z         
2025-03-14T05:04:18.2079919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:04:18.2080432Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:04:18.2080666Z         
2025-03-14T05:04:32.0960206Z Compilation time (from dynamo_timed): 34.87895761
2025-03-14T05:04:32.0963027Z pass
2025-03-14T05:04:32.0965122Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:04:32.0968789Z TIMING: entire_frame_compile:34.87896 gc:0.04413 _recursive_pre_grad_passes:0.03414 async_compile.wait:3.78731 backend_compile:20.09434 _recursive_joint_graph_passes:0.17148 _recursive_post_grad_passes:0.08548 code_gen:7.35657 inductor_compile:8.71407 total_wall_time:34.87896
2025-03-14T05:04:32.0973544Z STATS: call_* op count: 990 | FakeTensorMode.__torch_dispatch__:22926 | FakeTensor.__torch_dispatch__:1797 | ProxyTorchDispatchMode.__torch_dispatch__:5692 | attempt fast:112 | slow no contiguity match:36 | fast is_contiguous:76
2025-03-14T05:04:32.0975333Z Dynamo produced 61 graphs covering 990 ops with 46 graph breaks (6 unique)
2025-03-14T05:04:37.8132426Z 
2025-03-14T05:04:46.2595424Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:04:46.2595924Z loading model: 0it [00:08, ?it/s]
2025-03-14T05:04:46.2609634Z cpu  eval  detectron2_fcos_r_50_fpn           
2025-03-14T05:05:05.4211468Z WARNING:common:fp64 golden ref were not generated for detectron2_fcos_r_50_fpn. Setting accuracy check to cosine
2025-03-14T05:05:05.4274454Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:16.2277403Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:05:22.5700557Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:21.1445478Z Compilation time (from dynamo_timed): 51.94651737
2025-03-14T05:06:21.1450628Z pass
2025-03-14T05:06:21.1453137Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:06:21.1454069Z TIMING: entire_frame_compile:51.94652 gc:0.02826 _recursive_pre_grad_passes:0.02577 async_compile.wait:15.56742 backend_compile:40.0234 _recursive_joint_graph_passes:0.29503 _recursive_post_grad_passes:0.19654 code_gen:23.55719 inductor_compile:27.01608 total_wall_time:51.94652
2025-03-14T05:06:21.1460301Z STATS: call_* op count: 944 | FakeTensorMode.__torch_dispatch__:29063 | FakeTensor.__torch_dispatch__:3334 | ProxyTorchDispatchMode.__torch_dispatch__:10972
2025-03-14T05:06:21.1462256Z Dynamo produced 29 graphs covering 944 ops with 22 graph breaks (4 unique)
2025-03-14T05:06:26.9790257Z 
2025-03-14T05:06:43.5446045Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:06:43.5446878Z loading model: 0it [00:16, ?it/s]
2025-03-14T05:06:43.5458373Z cpu  eval  detectron2_maskrcnn_r_101_c4       
2025-03-14T05:06:53.3760785Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_101_c4. Setting accuracy check to cosine
2025-03-14T05:06:53.3763598Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:15.5931687Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:35.4763242Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:07:46.7993733Z 
2025-03-14T05:07:46.7994351Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:46.8112047Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", 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L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:07:46.8200578Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:07:46.8201077Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8201855Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8202570Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8203241Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8203879Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8204502Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8205174Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8205903Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8206611Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8207355Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8207999Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8208701Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8209435Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8210147Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8210826Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8211466Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8212136Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8212910Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8213608Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8214279Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8214929Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.8215622Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8216361Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8217083Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.8217782Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.8218427Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8219087Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8219803Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8220493Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8221189Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8221897Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8222607Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8223471Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8224383Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8225210Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8225935Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8226737Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8227608Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8228484Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8229755Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8230509Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8231310Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8232123Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8232905Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8233658Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8234368Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8235118Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8235897Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8236628Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8237308Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8237979Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8238645Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8239364Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8240422Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8241088Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8241755Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8242433Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8243165Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8243869Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8244541Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8245180Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8245853Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8246847Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8247546Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8248218Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8248856Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8249523Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8250236Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8250993Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8251686Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8252340Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.8253028Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8253775Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8254511Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.8255217Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.8255898Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8256567Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8257281Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8257970Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8258637Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8259278Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8259959Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8260688Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8261397Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8262084Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8262735Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8263421Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8264235Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8264976Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8265732Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8266387Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8267314Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8268071Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8268792Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8269518Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8270196Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8270876Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8271607Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8272314Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8272995Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8273657Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8274336Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8275074Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8275790Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8276478Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8277122Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8278141Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8278889Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8279844Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8280538Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8281193Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8282349Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8283101Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8283828Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8284588Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8285233Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8285906Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8286645Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8287351Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8288030Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8288669Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8289339Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8290391Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8291106Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8291827Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8292769Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8293525Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8294239Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8294969Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8295641Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8296274Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8296939Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8297655Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8298363Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8299056Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8299708Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.8300402Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8301145Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8301900Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.8302622Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.8303288Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8303965Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8304762Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8305488Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8306189Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8306900Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8307645Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8308485Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8309302Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8310044Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8310749Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8311509Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8312344Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8313137Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8313868Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8314563Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8315266Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8316018Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8316747Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8317453Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8318125Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8318842Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8319567Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8320263Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8320929Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8321591Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8322279Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8322991Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8323681Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8324344Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8324983Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8325646Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8326389Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8327088Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8327756Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8328388Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8329051Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8329767Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8330464Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8331144Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8331778Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8332443Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8333155Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8333850Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8334542Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8335177Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8335854Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8336569Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8337259Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8337927Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8338558Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8339237Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8339980Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8340679Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8341354Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8341995Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8342669Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8343397Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8344102Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8344904Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8345561Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8346265Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8347011Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8347798Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8348552Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8349274Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8350040Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8350853Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8351651Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8352383Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8353070Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8353863Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8354675Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8355481Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8356225Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8356899Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8357598Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8358342Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8359058Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8359763Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8360399Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8361059Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8361778Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8362509Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8363189Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8363846Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8364513Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8365238Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8365929Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8366599Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8367278Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8367977Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8368722Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8369442Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8370124Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8371717Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8372400Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8373351Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8374057Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8374734Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8375377Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8376048Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8376793Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8377492Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8378178Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8378822Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8379486Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8380209Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8380903Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8382046Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8382786Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8383475Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8384485Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8385205Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8385900Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8386543Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8387209Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8387932Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8388631Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8389310Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8389952Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8390618Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8391369Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8392061Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8392750Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8393396Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8394065Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8395072Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8395778Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8396737Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8397396Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8398070Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8398786Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8399482Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8400156Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8400798Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8401474Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8402188Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8402886Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8403570Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8404216Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8404931Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8405673Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8406433Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8407318Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8407982Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8408677Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8409427Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8410175Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8410892Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8411555Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8412247Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8413022Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8413745Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8414438Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8415092Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8415782Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8416523Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8417512Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8418222Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8418911Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8419613Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8420369Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8421108Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8421789Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8422453Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8423126Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8424303Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8425077Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8425774Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8426432Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8427116Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8427864Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8428590Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8429281Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8429937Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8430626Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8431370Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8432088Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8432783Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8433452Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8434134Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8434892Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8435621Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8436327Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8436993Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8437682Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8438450Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8439163Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8439861Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8440521Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8443284Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8444057Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8444783Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8445494Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8446162Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8446861Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8447616Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8448341Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8449065Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8449728Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8450583Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8451338Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8452060Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8452756Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8453413Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8454131Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8454894Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8455608Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8456301Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8457097Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8457799Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8458539Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8459251Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8459941Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8460600Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8461293Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8462025Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8462776Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8463470Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8464211Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8464931Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8465685Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8466421Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8467439Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8468124Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8468874Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8469642Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8470390Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8471098Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8471772Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8472473Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8473230Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8473968Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8474683Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8475360Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8476065Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8476832Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8477620Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8478351Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8479028Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8479735Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8480480Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8481193Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8482230Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8482966Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8483657Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8484397Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8485114Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8485810Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8486474Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8487166Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8487904Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8488621Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8489314Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8489976Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8490662Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8491436Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8492763Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8493476Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8494144Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8494835Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8495578Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8496298Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8497013Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8497694Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8498375Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8499114Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8499826Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8500514Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8501165Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8501843Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8502575Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8503293Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8503979Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8504701Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8505417Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8506160Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8506897Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8507595Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8508266Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8508955Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8509699Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8510432Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8511155Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8511813Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8512502Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8513249Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8513967Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8514658Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8515316Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8516033Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8516779Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8517506Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8518200Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8518874Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8519561Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8520313Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8521036Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8521727Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8522388Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8523091Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8523835Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8524564Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8525243Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8525903Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8526599Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8527323Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8528027Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8528697Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8529336Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8530003Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8530721Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8531413Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8532083Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8532748Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:07:46.8533448Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8534176Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8534873Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.8535545Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.8536185Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:07:46.8536854Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8537596Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8538304Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.8538978Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.8539618Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:07:46.8540292Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.8541013Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.8541711Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.8542388Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.8543094Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:07:46.8543812Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:07:46.8544600Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:07:46.8545342Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:07:46.8546159Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:07:46.8546943Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:07:46.8547693Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:07:46.8548171Z         
2025-03-14T05:07:46.8548580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8549392Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8550000Z         
2025-03-14T05:07:46.8550374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8552197Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8553788Z         
2025-03-14T05:07:46.8554183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:07:46.8554682Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:07:46.8554957Z         
2025-03-14T05:07:46.8555419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:07:46.8556087Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:07:46.8556453Z         
2025-03-14T05:07:46.8556808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8557554Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8558586Z         
2025-03-14T05:07:46.8558946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8560745Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8562370Z         
2025-03-14T05:07:46.8562746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8563222Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:07:46.8563476Z         
2025-03-14T05:07:46.8563815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8564532Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8565069Z         
2025-03-14T05:07:46.8565450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8567287Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8569840Z         
2025-03-14T05:07:46.8570239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8570997Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:07:46.8571274Z         
2025-03-14T05:07:46.8571621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8572373Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8572942Z         
2025-03-14T05:07:46.8573304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8575150Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8576836Z         
2025-03-14T05:07:46.8577174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8577914Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.8578465Z         
2025-03-14T05:07:46.8579047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8580983Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8582905Z         
2025-03-14T05:07:46.8583285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8583768Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:07:46.8584035Z         
2025-03-14T05:07:46.8584457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8584957Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:07:46.8585231Z         
2025-03-14T05:07:46.8585572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8586306Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8586848Z         
2025-03-14T05:07:46.8587202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8589058Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8590739Z         
2025-03-14T05:07:46.8591111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8591598Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:07:46.8591853Z         
2025-03-14T05:07:46.8592761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8593481Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8594022Z         
2025-03-14T05:07:46.8594371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8596230Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8597814Z         
2025-03-14T05:07:46.8598186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8598661Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:07:46.8598915Z         
2025-03-14T05:07:46.8599246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8599967Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8600507Z         
2025-03-14T05:07:46.8600852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8602643Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8604241Z         
2025-03-14T05:07:46.8604603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8605106Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:07:46.8605377Z         
2025-03-14T05:07:46.8605744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8606228Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:07:46.8606494Z         
2025-03-14T05:07:46.8606827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8607543Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8608067Z         
2025-03-14T05:07:46.8608409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8610239Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8611817Z         
2025-03-14T05:07:46.8612182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8612654Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:07:46.8612911Z         
2025-03-14T05:07:46.8613242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8613954Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8614485Z         
2025-03-14T05:07:46.8614825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8616614Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8618263Z         
2025-03-14T05:07:46.8618627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8619098Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:07:46.8619355Z         
2025-03-14T05:07:46.8619688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8620411Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8620950Z         
2025-03-14T05:07:46.8621293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8623102Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8624752Z         
2025-03-14T05:07:46.8625118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8625613Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:07:46.8625888Z         
2025-03-14T05:07:46.8626259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8626759Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:07:46.8627034Z         
2025-03-14T05:07:46.8627376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8628113Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8628657Z         
2025-03-14T05:07:46.8629013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8630862Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8632526Z         
2025-03-14T05:07:46.8632901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8633390Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:07:46.8633654Z         
2025-03-14T05:07:46.8633989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8634732Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8635285Z         
2025-03-14T05:07:46.8635655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8637529Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8639146Z         
2025-03-14T05:07:46.8639521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8640008Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:07:46.8640287Z         
2025-03-14T05:07:46.8640617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8641337Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8643425Z         
2025-03-14T05:07:46.8644684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8650208Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8654929Z         
2025-03-14T05:07:46.8656097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8657034Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.8660800Z         
2025-03-14T05:07:46.8661271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8663533Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8665574Z         
2025-03-14T05:07:46.8665993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8666540Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:07:46.8666841Z         
2025-03-14T05:07:46.8667259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8667811Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:07:46.8668117Z         
2025-03-14T05:07:46.8668496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8669309Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8669916Z         
2025-03-14T05:07:46.8670307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8672214Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8673861Z         
2025-03-14T05:07:46.8674236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8695514Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:07:46.8695832Z         
2025-03-14T05:07:46.8696270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8697078Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8697667Z         
2025-03-14T05:07:46.8698039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8700143Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8701836Z         
2025-03-14T05:07:46.8702244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8702765Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:07:46.8703044Z         
2025-03-14T05:07:46.8703409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8704281Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8704865Z         
2025-03-14T05:07:46.8705243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8707207Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8708952Z         
2025-03-14T05:07:46.8709341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8709887Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:07:46.8710177Z         
2025-03-14T05:07:46.8710569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8711091Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:07:46.8711390Z         
2025-03-14T05:07:46.8711731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8712461Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8712994Z         
2025-03-14T05:07:46.8713348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8715173Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8716771Z         
2025-03-14T05:07:46.8717141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8717626Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:07:46.8717886Z         
2025-03-14T05:07:46.8718220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8718946Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8719484Z         
2025-03-14T05:07:46.8719830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8721616Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8723235Z         
2025-03-14T05:07:46.8723598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8724543Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:07:46.8724806Z         
2025-03-14T05:07:46.8725141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8725867Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8726406Z         
2025-03-14T05:07:46.8726753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8728585Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8730207Z         
2025-03-14T05:07:46.8730586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8731128Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:07:46.8731397Z         
2025-03-14T05:07:46.8731762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8732245Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:07:46.8732511Z         
2025-03-14T05:07:46.8732849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8733566Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8734091Z         
2025-03-14T05:07:46.8734434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8736212Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8737849Z         
2025-03-14T05:07:46.8738214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8738686Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:07:46.8738950Z         
2025-03-14T05:07:46.8739288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8740027Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8740573Z         
2025-03-14T05:07:46.8740926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8742806Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8744503Z         
2025-03-14T05:07:46.8744901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8745416Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:07:46.8745693Z         
2025-03-14T05:07:46.8746045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8746792Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8747347Z         
2025-03-14T05:07:46.8747702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8749561Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8751219Z         
2025-03-14T05:07:46.8751592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8752085Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:07:46.8752361Z         
2025-03-14T05:07:46.8752733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8753226Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:07:46.8753499Z         
2025-03-14T05:07:46.8753837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8754575Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8755118Z         
2025-03-14T05:07:46.8755491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8757338Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8758962Z         
2025-03-14T05:07:46.8759333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8759814Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:07:46.8760076Z         
2025-03-14T05:07:46.8760415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8761146Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8761698Z         
2025-03-14T05:07:46.8762039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8763880Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8765521Z         
2025-03-14T05:07:46.8765898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8766377Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:07:46.8766636Z         
2025-03-14T05:07:46.8766976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8767714Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8768256Z         
2025-03-14T05:07:46.8768609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8770492Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8772107Z         
2025-03-14T05:07:46.8772440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8773188Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.8773738Z         
2025-03-14T05:07:46.8774089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8775948Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8777621Z         
2025-03-14T05:07:46.8777979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8778443Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:07:46.8778712Z         
2025-03-14T05:07:46.8779076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8779553Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:07:46.8779812Z         
2025-03-14T05:07:46.8780136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8780842Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8781360Z         
2025-03-14T05:07:46.8781889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8783777Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8785663Z         
2025-03-14T05:07:46.8786062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8786551Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:07:46.8786813Z         
2025-03-14T05:07:46.8787158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8787897Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8788450Z         
2025-03-14T05:07:46.8788808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8790670Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8792756Z         
2025-03-14T05:07:46.8793162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8794352Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:07:46.8794611Z         
2025-03-14T05:07:46.8794945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8795660Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8796195Z         
2025-03-14T05:07:46.8796544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8798368Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8799956Z         
2025-03-14T05:07:46.8800317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8800797Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:07:46.8801066Z         
2025-03-14T05:07:46.8801436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8801918Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:07:46.8802180Z         
2025-03-14T05:07:46.8802516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8803234Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8803767Z         
2025-03-14T05:07:46.8804115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8805932Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8807579Z         
2025-03-14T05:07:46.8807951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8808433Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:07:46.8808693Z         
2025-03-14T05:07:46.8809030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8809751Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8810287Z         
2025-03-14T05:07:46.8810637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8812487Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8814054Z         
2025-03-14T05:07:46.8814413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8814875Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:07:46.8815127Z         
2025-03-14T05:07:46.8815458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8816169Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8816693Z         
2025-03-14T05:07:46.8817033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8818809Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8820396Z         
2025-03-14T05:07:46.8820756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8821243Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:07:46.8821504Z         
2025-03-14T05:07:46.8821869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8822340Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:07:46.8822589Z         
2025-03-14T05:07:46.8822921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8823629Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8824225Z         
2025-03-14T05:07:46.8824974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8826928Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8828531Z         
2025-03-14T05:07:46.8828905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8829382Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:07:46.8829640Z         
2025-03-14T05:07:46.8829976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8830705Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8831757Z         
2025-03-14T05:07:46.8832118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8833966Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8835946Z         
2025-03-14T05:07:46.8836327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8836820Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:07:46.8837079Z         
2025-03-14T05:07:46.8837423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8838488Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8839050Z         
2025-03-14T05:07:46.8839408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8845325Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8847000Z         
2025-03-14T05:07:46.8847394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8847893Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:07:46.8848165Z         
2025-03-14T05:07:46.8848546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8849040Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:07:46.8849309Z         
2025-03-14T05:07:46.8849646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8850990Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8851854Z         
2025-03-14T05:07:46.8852224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8856120Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8862122Z         
2025-03-14T05:07:46.8862754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8863954Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:07:46.8864561Z         
2025-03-14T05:07:46.8865651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8869724Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8870339Z         
2025-03-14T05:07:46.8870745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8872862Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8874589Z         
2025-03-14T05:07:46.8874988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8875497Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:07:46.8875775Z         
2025-03-14T05:07:46.8876131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8876913Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8877495Z         
2025-03-14T05:07:46.8877868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8879804Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8881653Z         
2025-03-14T05:07:46.8882047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8882564Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:07:46.8882848Z         
2025-03-14T05:07:46.8883241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8883756Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:07:46.8884040Z         
2025-03-14T05:07:46.8884397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8885168Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8885733Z         
2025-03-14T05:07:46.8886121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8888174Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8889847Z         
2025-03-14T05:07:46.8890240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8890741Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:07:46.8891015Z         
2025-03-14T05:07:46.8892307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8893152Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8893738Z         
2025-03-14T05:07:46.8894121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8896092Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8897902Z         
2025-03-14T05:07:46.8898311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8898831Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:07:46.8899107Z         
2025-03-14T05:07:46.8899465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8900270Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8900861Z         
2025-03-14T05:07:46.8901238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8903772Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8905908Z         
2025-03-14T05:07:46.8906335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8906891Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:07:46.8907169Z         
2025-03-14T05:07:46.8907559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8908070Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:07:46.8908343Z         
2025-03-14T05:07:46.8908708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8909470Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8910002Z         
2025-03-14T05:07:46.8910353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8912180Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8913831Z         
2025-03-14T05:07:46.8914199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8914672Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:07:46.8914932Z         
2025-03-14T05:07:46.8915270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8916000Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8916547Z         
2025-03-14T05:07:46.8916896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8918765Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8920395Z         
2025-03-14T05:07:46.8920765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8921239Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:07:46.8921495Z         
2025-03-14T05:07:46.8921827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8922560Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8923105Z         
2025-03-14T05:07:46.8923455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8925307Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8926968Z         
2025-03-14T05:07:46.8927342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8927831Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:07:46.8928102Z         
2025-03-14T05:07:46.8928477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8928966Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:07:46.8929231Z         
2025-03-14T05:07:46.8929571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8930300Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8930833Z         
2025-03-14T05:07:46.8931184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8933074Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8934683Z         
2025-03-14T05:07:46.8935050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8935521Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:07:46.8935774Z         
2025-03-14T05:07:46.8936111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8936834Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8937367Z         
2025-03-14T05:07:46.8937715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8939532Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8941184Z         
2025-03-14T05:07:46.8941552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8942026Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:07:46.8942286Z         
2025-03-14T05:07:46.8942622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8943360Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8943905Z         
2025-03-14T05:07:46.8944367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8946299Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8947919Z         
2025-03-14T05:07:46.8948290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8948769Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:07:46.8949039Z         
2025-03-14T05:07:46.8949405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8949883Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:07:46.8950139Z         
2025-03-14T05:07:46.8950469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8951193Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8951744Z         
2025-03-14T05:07:46.8952083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8953956Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8955592Z         
2025-03-14T05:07:46.8955967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8956453Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:07:46.8956716Z         
2025-03-14T05:07:46.8957057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8957788Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8958327Z         
2025-03-14T05:07:46.8958682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8960553Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8962164Z         
2025-03-14T05:07:46.8962536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8963017Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:07:46.8963275Z         
2025-03-14T05:07:46.8963618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8964343Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8964882Z         
2025-03-14T05:07:46.8965232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8967059Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8969218Z         
2025-03-14T05:07:46.8969604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8970086Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:07:46.8970354Z         
2025-03-14T05:07:46.8970716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8971187Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:07:46.8971447Z         
2025-03-14T05:07:46.8971772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8972477Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8972992Z         
2025-03-14T05:07:46.8973332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8975179Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8976814Z         
2025-03-14T05:07:46.8977184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8977664Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:07:46.8977928Z         
2025-03-14T05:07:46.8978262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8978989Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8979531Z         
2025-03-14T05:07:46.8979880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8982247Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8983956Z         
2025-03-14T05:07:46.8984469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8985015Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:07:46.8985303Z         
2025-03-14T05:07:46.8985686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8986483Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.8987091Z         
2025-03-14T05:07:46.8987486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8989599Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8991391Z         
2025-03-14T05:07:46.8992320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.8992913Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:07:46.8993215Z         
2025-03-14T05:07:46.8993636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8994190Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:07:46.8994465Z         
2025-03-14T05:07:46.8994821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8995587Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.8996193Z         
2025-03-14T05:07:46.8996560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.8998191Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.8998283Z         
2025-03-14T05:07:46.8998595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.8998755Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:07:46.8998825Z         
2025-03-14T05:07:46.8999101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.8999550Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.8999630Z         
2025-03-14T05:07:46.8999909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9001580Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9001663Z         
2025-03-14T05:07:46.9001962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9002120Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:07:46.9002190Z         
2025-03-14T05:07:46.9002459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9002909Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9002986Z         
2025-03-14T05:07:46.9003264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9004808Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9004900Z         
2025-03-14T05:07:46.9005174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9005333Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:07:46.9005398Z         
2025-03-14T05:07:46.9005681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9005820Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:07:46.9005890Z         
2025-03-14T05:07:46.9006133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9006549Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9006614Z         
2025-03-14T05:07:46.9006911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9008395Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9008470Z         
2025-03-14T05:07:46.9008763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9008903Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:07:46.9008991Z         
2025-03-14T05:07:46.9009253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9009672Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9009759Z         
2025-03-14T05:07:46.9010019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9011488Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9011583Z         
2025-03-14T05:07:46.9011876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9012017Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:07:46.9012089Z         
2025-03-14T05:07:46.9012336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9012771Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9012841Z         
2025-03-14T05:07:46.9013111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9014682Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9014752Z         
2025-03-14T05:07:46.9015044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9015195Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:07:46.9015269Z         
2025-03-14T05:07:46.9015554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9015699Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:07:46.9015766Z         
2025-03-14T05:07:46.9016015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9016447Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9016520Z         
2025-03-14T05:07:46.9016793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9018286Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9018359Z         
2025-03-14T05:07:46.9018643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9018785Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:07:46.9018850Z         
2025-03-14T05:07:46.9019104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9019522Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9019596Z         
2025-03-14T05:07:46.9019888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9021415Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9021490Z         
2025-03-14T05:07:46.9021770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9021914Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:07:46.9021979Z         
2025-03-14T05:07:46.9022235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9022655Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9022742Z         
2025-03-14T05:07:46.9023009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9024655Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9024742Z         
2025-03-14T05:07:46.9025043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9025217Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:07:46.9025281Z         
2025-03-14T05:07:46.9025568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9025708Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:07:46.9025785Z         
2025-03-14T05:07:46.9026033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9026490Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9026559Z         
2025-03-14T05:07:46.9026829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9028367Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9028435Z         
2025-03-14T05:07:46.9028724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9028860Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:07:46.9028932Z         
2025-03-14T05:07:46.9029179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9029630Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9029722Z         
2025-03-14T05:07:46.9029983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9031520Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9031588Z         
2025-03-14T05:07:46.9031882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9032018Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:07:46.9032090Z         
2025-03-14T05:07:46.9032336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9032767Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9032874Z         
2025-03-14T05:07:46.9033143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9034684Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9034751Z         
2025-03-14T05:07:46.9035037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9035183Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:07:46.9035255Z         
2025-03-14T05:07:46.9035532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9035678Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:07:46.9035769Z         
2025-03-14T05:07:46.9036017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9036437Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9036519Z         
2025-03-14T05:07:46.9036787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9038309Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9038388Z         
2025-03-14T05:07:46.9038678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9038813Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:07:46.9038885Z         
2025-03-14T05:07:46.9039133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9039603Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9039672Z         
2025-03-14T05:07:46.9039943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9041473Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9041550Z         
2025-03-14T05:07:46.9041840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9041976Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:07:46.9042049Z         
2025-03-14T05:07:46.9042297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9042744Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9042825Z         
2025-03-14T05:07:46.9043106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9044607Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9044680Z         
2025-03-14T05:07:46.9044963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9045109Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:07:46.9045182Z         
2025-03-14T05:07:46.9045456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9045604Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:07:46.9045666Z         
2025-03-14T05:07:46.9045995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9046399Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9046471Z         
2025-03-14T05:07:46.9046725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9048198Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9048273Z         
2025-03-14T05:07:46.9048548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9048705Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:07:46.9048769Z         
2025-03-14T05:07:46.9049019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9049483Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9049554Z         
2025-03-14T05:07:46.9049806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9051314Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9051388Z         
2025-03-14T05:07:46.9051668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9051809Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:07:46.9051874Z         
2025-03-14T05:07:46.9052129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9052582Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9052658Z         
2025-03-14T05:07:46.9052924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9054486Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9054561Z         
2025-03-14T05:07:46.9054851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9055004Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:07:46.9055083Z         
2025-03-14T05:07:46.9055370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9055511Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:07:46.9055598Z         
2025-03-14T05:07:46.9055839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9056247Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9056311Z         
2025-03-14T05:07:46.9056573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9058102Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9058172Z         
2025-03-14T05:07:46.9058463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9058600Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:07:46.9058673Z         
2025-03-14T05:07:46.9058949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9059379Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9059445Z         
2025-03-14T05:07:46.9059713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9061240Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9061308Z         
2025-03-14T05:07:46.9061596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9061745Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:07:46.9061821Z         
2025-03-14T05:07:46.9062071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9062513Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9062578Z         
2025-03-14T05:07:46.9062851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9064515Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9064597Z         
2025-03-14T05:07:46.9064906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9065064Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:07:46.9065153Z         
2025-03-14T05:07:46.9065938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9066099Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:07:46.9066168Z         
2025-03-14T05:07:46.9066442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9066854Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9066928Z         
2025-03-14T05:07:46.9067197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9068717Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9068814Z         
2025-03-14T05:07:46.9069103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9069252Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:07:46.9069333Z         
2025-03-14T05:07:46.9070007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9070449Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9070517Z         
2025-03-14T05:07:46.9070790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9072318Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9072397Z         
2025-03-14T05:07:46.9072682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9072826Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:07:46.9072932Z         
2025-03-14T05:07:46.9073187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9073614Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9073679Z         
2025-03-14T05:07:46.9073948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9075471Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9075544Z         
2025-03-14T05:07:46.9075827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9076366Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:07:46.9076450Z         
2025-03-14T05:07:46.9076743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9076918Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:07:46.9076985Z         
2025-03-14T05:07:46.9077243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9077660Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9077734Z         
2025-03-14T05:07:46.9078196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9079717Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9079795Z         
2025-03-14T05:07:46.9080106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9080255Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:07:46.9080319Z         
2025-03-14T05:07:46.9080569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9080987Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9081060Z         
2025-03-14T05:07:46.9081316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9082964Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9083089Z         
2025-03-14T05:07:46.9083377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9083549Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:07:46.9083614Z         
2025-03-14T05:07:46.9083870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9084295Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9084369Z         
2025-03-14T05:07:46.9084631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9086150Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9086226Z         
2025-03-14T05:07:46.9086506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9086716Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:07:46.9086786Z         
2025-03-14T05:07:46.9087078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9087226Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:07:46.9087302Z         
2025-03-14T05:07:46.9087548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9087967Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9088033Z         
2025-03-14T05:07:46.9088305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9089838Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9089952Z         
2025-03-14T05:07:46.9090242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9090382Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:07:46.9090453Z         
2025-03-14T05:07:46.9090703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9091131Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9091197Z         
2025-03-14T05:07:46.9091469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9093439Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9093532Z         
2025-03-14T05:07:46.9093912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9094063Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:07:46.9094143Z         
2025-03-14T05:07:46.9094407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9094857Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9094932Z         
2025-03-14T05:07:46.9095235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9096946Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9097042Z         
2025-03-14T05:07:46.9097366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9097565Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:07:46.9097647Z         
2025-03-14T05:07:46.9097965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9098137Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:07:46.9098210Z         
2025-03-14T05:07:46.9098499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9098969Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9099054Z         
2025-03-14T05:07:46.9099338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9100855Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9100932Z         
2025-03-14T05:07:46.9101212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9101358Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:07:46.9101421Z         
2025-03-14T05:07:46.9101671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9102091Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9102168Z         
2025-03-14T05:07:46.9102424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9103916Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9104023Z         
2025-03-14T05:07:46.9104362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9104511Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:07:46.9104579Z         
2025-03-14T05:07:46.9104832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9105271Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9105353Z         
2025-03-14T05:07:46.9105637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9107231Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9107310Z         
2025-03-14T05:07:46.9107589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9107754Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:07:46.9107819Z         
2025-03-14T05:07:46.9108111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9108254Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:07:46.9108327Z         
2025-03-14T05:07:46.9108573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9108998Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9109071Z         
2025-03-14T05:07:46.9109332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9110852Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9110960Z         
2025-03-14T05:07:46.9111250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9111393Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:07:46.9111457Z         
2025-03-14T05:07:46.9111712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9112139Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9112212Z         
2025-03-14T05:07:46.9112471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9114015Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9114092Z         
2025-03-14T05:07:46.9114378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9114521Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:07:46.9114588Z         
2025-03-14T05:07:46.9114844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9115279Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9115353Z         
2025-03-14T05:07:46.9115610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9117096Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9117217Z         
2025-03-14T05:07:46.9117490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9117649Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:07:46.9117713Z         
2025-03-14T05:07:46.9117998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9118140Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:07:46.9118213Z         
2025-03-14T05:07:46.9118457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9118869Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9118933Z         
2025-03-14T05:07:46.9119195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9120681Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9120758Z         
2025-03-14T05:07:46.9121050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9121185Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:07:46.9121256Z         
2025-03-14T05:07:46.9121495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9121920Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9121986Z         
2025-03-14T05:07:46.9122246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9123739Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9123853Z         
2025-03-14T05:07:46.9124137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9124271Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:07:46.9124345Z         
2025-03-14T05:07:46.9124586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9125009Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9125074Z         
2025-03-14T05:07:46.9125336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9126880Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9126950Z         
2025-03-14T05:07:46.9127232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9127389Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:07:46.9127465Z         
2025-03-14T05:07:46.9127747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9127896Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:07:46.9127968Z         
2025-03-14T05:07:46.9128409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:46.9128567Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:07:46.9128642Z         
2025-03-14T05:07:46.9128934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9129082Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:46.9129165Z         
2025-03-14T05:07:46.9129606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:46.9129773Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:07:46.9129844Z         
2025-03-14T05:07:46.9130136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9130283Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:07:46.9130346Z         
2025-03-14T05:07:46.9130725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:07:46.9130907Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:07:46.9131012Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:07:46.9131136Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:07:46.9131209Z         
2025-03-14T05:07:46.9131537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:07:46.9131672Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:07:46.9131737Z         
2025-03-14T05:07:46.9132068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:07:46.9132197Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:07:46.9132263Z         
2025-03-14T05:07:46.9132651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:07:46.9132896Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:07:46.9132969Z         
2025-03-14T05:07:46.9133385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:07:46.9133518Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:07:46.9133938Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:07:46.9134070Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:07:46.9134190Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:07:46.9134264Z         
2025-03-14T05:07:46.9134564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:46.9134696Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T05:07:46.9134762Z         
2025-03-14T05:07:46.9135020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9135788Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:07:46.9135896Z         
2025-03-14T05:07:46.9136171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:07:46.9136371Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T05:07:46.9136438Z         
2025-03-14T05:07:46.9136824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:07:46.9137676Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:07:46.9137746Z         
2025-03-14T05:07:46.9138112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:07:46.9138913Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:07:46.9138990Z         
2025-03-14T05:07:46.9139364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:07:46.9139526Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:07:46.9139674Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:07:46.9139739Z         
2025-03-14T05:07:46.9140164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:07:46.9140319Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T05:07:46.9140493Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:07:46.9140667Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:07:46.9140741Z         
2025-03-14T05:07:46.9141130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:46.9141343Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:07:46.9141408Z         
2025-03-14T05:07:46.9141840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:46.9142007Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:07:46.9142166Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:46.9142323Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:46.9142396Z         
2025-03-14T05:07:46.9142772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:46.9142947Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:07:46.9143013Z         
2025-03-14T05:07:46.9143328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:46.9143468Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:46.9143543Z         
2025-03-14T05:07:46.9143856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:46.9143995Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:46.9144175Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:46.9144343Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:46.9144409Z         
2025-03-14T05:07:46.9144739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:46.9144867Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:46.9144997Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:46.9145144Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:07:46.9145264Z         
2025-03-14T05:07:46.9145575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:46.9145709Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:46.9145799Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:46.9145930Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:07:46.9145995Z         
2025-03-14T05:07:46.9146313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:46.9146459Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:46.9146560Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:46.9146689Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:07:46.9146763Z         
2025-03-14T05:07:46.9147097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:46.9147258Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:46.9147381Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:46.9147446Z         
2025-03-14T05:07:46.9147753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:46.9147923Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:46.9148042Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:46.9148108Z         
2025-03-14T05:07:46.9148412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:46.9148578Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:46.9148698Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:07:46.9148763Z         
2025-03-14T05:07:46.9149066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:46.9149248Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:46.9149366Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:07:46.9149430Z         
2025-03-14T05:07:46.9149778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:46.9149915Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:46.9149987Z         
2025-03-14T05:07:46.9150304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:46.9150442Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:46.9150505Z         
2025-03-14T05:07:46.9150844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:46.9150979Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:46.9151145Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:07:46.9151294Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:46.9151436Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:07:46.9151499Z         
2025-03-14T05:07:46.9151837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:46.9151970Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:46.9152098Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:07:46.9152242Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:46.9152381Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:07:46.9152448Z         
2025-03-14T05:07:46.9152773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:46.9152898Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:46.9153052Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:46.9153189Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:07:46.9153253Z         
2025-03-14T05:07:46.9153582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:46.9153748Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:46.9153918Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:46.9154065Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:07:46.9154137Z         
2025-03-14T05:07:46.9154441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:46.9154543Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:46.9154656Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:46.9154728Z         
2025-03-14T05:07:46.9155026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:46.9155127Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:46.9155239Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:46.9155312Z         
2025-03-14T05:07:46.9155611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:46.9155729Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:46.9155852Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:46.9155922Z         
2025-03-14T05:07:46.9156213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:46.9156330Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:46.9156453Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:46.9156524Z         
2025-03-14T05:07:46.9156884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:46.9157069Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:46.9157133Z         
2025-03-14T05:07:46.9157468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:46.9157622Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:46.9157693Z         
2025-03-14T05:07:46.9158066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:46.9158244Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:46.9158310Z         
2025-03-14T05:07:46.9158793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:46.9158926Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:46.9158998Z         
2025-03-14T05:07:46.9159288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9159434Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:07:46.9159514Z         
2025-03-14T05:07:46.9159950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:46.9160082Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:07:46.9160194Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:46.9160305Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:46.9160377Z         
2025-03-14T05:07:46.9160828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:46.9160996Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:46.9161234Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:07:46.9161299Z         
2025-03-14T05:07:46.9161760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:46.9161923Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:46.9161994Z         
2025-03-14T05:07:46.9162282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9162438Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:46.9162501Z         
2025-03-14T05:07:46.9162883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:46.9163701Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:46.9163782Z         
2025-03-14T05:07:46.9164073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:46.9164221Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:07:46.9164288Z         
2025-03-14T05:07:46.9164658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:46.9164795Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:46.9164870Z         
2025-03-14T05:07:46.9165339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:46.9165480Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:07:46.9165599Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:46.9165760Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:46.9165890Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:46.9165960Z         
2025-03-14T05:07:46.9166316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:46.9166457Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:46.9166520Z         
2025-03-14T05:07:46.9167043Z 
2025-03-14T05:07:46.9167144Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:46.9257766Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", 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"f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:07:46.9258448Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:07:46.9258728Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9259059Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9259390Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9259712Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9260005Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9260288Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9260633Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9260966Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9261289Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9261591Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9261878Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9262216Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9262698Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9263099Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9263486Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9263808Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9264287Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9264690Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9265080Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9265435Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9265809Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.9266276Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9266703Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9267125Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.9267527Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.9267842Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9268261Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9268642Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9269058Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9269437Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9269759Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9270180Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9270629Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9270983Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9271285Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9271570Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9271904Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9272244Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9272554Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9272867Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9273153Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9273499Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9273853Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9274162Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9274469Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9274746Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9275085Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9275459Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9275812Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9276164Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9276461Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9276827Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9277154Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9277496Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9277842Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9278166Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9278537Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9278912Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9279271Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9279618Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9279949Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9280321Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9280709Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9281056Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9281402Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9281838Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9282227Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9282601Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9282950Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9283276Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9283566Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.9283967Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9284311Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9284650Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.9284966Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.9285258Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9285598Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9285929Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9286250Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9286568Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9286855Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9287212Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9287548Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9287855Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9288166Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9288454Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9288785Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9289120Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9289430Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9289740Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9290043Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9290385Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9290707Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9291025Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9291338Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9292068Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9292556Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9292951Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9293314Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9293653Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9293960Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9294322Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9294662Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9294991Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9295299Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9295589Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9295925Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9296262Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9296578Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9296918Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9297196Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9297533Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9297865Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9298174Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9298489Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9298766Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9299100Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9299428Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9299762Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9300066Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9300369Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9300713Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9301043Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9301365Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9301673Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9301962Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9302295Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9302631Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9302945Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9303293Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9303578Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9303906Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9304274Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9304594Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9304912Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9305231Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:46.9305613Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9305983Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9306366Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:46.9306715Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:46.9306995Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9307340Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9307674Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9308002Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9308311Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9308603Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9308953Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9309287Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9309642Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9309956Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9310246Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9310581Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9310922Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9311239Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9311556Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9311847Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9312183Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9312546Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9312866Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9313215Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9313491Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9313828Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9314155Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9314474Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9314783Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9315057Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9315395Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9315724Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9316077Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9316382Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9316668Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9316998Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9317337Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9317651Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9317952Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9318235Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9318561Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9318920Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9319246Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9319555Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9319831Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9320170Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9320504Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9320813Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9321120Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9321395Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9321730Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9322083Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9322403Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9322707Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9322990Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9323329Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9323654Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9323970Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9324272Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9324556Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9324901Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9325242Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9325565Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9325871Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9326159Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9326490Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9326826Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9327138Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9327444Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9327719Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9328087Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9328415Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9328735Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9329043Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9329320Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9329662Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9329989Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9330309Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9330614Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9330915Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9331249Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9331604Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9331925Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9332235Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9332523Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9332861Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9333201Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9333512Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9333821Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9334101Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9334476Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9334812Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9335122Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9335436Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9335711Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9336055Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9336383Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9336698Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9337004Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9337311Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9337667Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9338012Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9338331Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9338634Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9338922Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9339251Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9339589Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9339899Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9340208Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9340528Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9340865Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9341199Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9341507Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9341817Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9342098Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9342440Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9342765Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9343085Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9343413Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9343691Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9344054Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9344455Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9344792Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9345106Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9345405Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9345757Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9346110Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9346442Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9346759Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9347090Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9347436Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9347785Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9348109Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9348437Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9348731Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9349111Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9349497Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9349866Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9350213Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9350515Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9350904Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9351277Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9351616Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9351977Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9352283Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9352678Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9353052Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9353432Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9353812Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9354121Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9354507Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9354885Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9355250Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9355621Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9355930Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9356279Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9356638Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9356974Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9357293Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9357594Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9357942Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9358274Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9358603Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9358924Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9359213Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9359563Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9359900Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9360264Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9360577Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9360869Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9361236Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9361579Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9361909Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9362222Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9362515Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9362855Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9363216Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9363539Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9363875Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9364158Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9364508Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9364854Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9365176Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9365497Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9365785Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9366133Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9366471Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9366907Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9367223Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9367520Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9367869Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9368211Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9368546Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9368860Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9369158Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9369503Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9369870Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9370210Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9370535Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9370838Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9371178Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9371516Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9371833Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9372143Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9372423Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9372773Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9373136Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9373468Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9373798Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9374076Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9374416Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9374748Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9375071Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9375374Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9375669Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9376025Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9376371Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9376719Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9377031Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9377334Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9377669Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9378007Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9378321Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9378637Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9378917Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9379285Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9379628Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9379950Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9380269Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9380556Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9380908Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9381245Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9382204Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9382524Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9382867Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9383221Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9383575Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9383906Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9384274Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9384586Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9384938Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9385295Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9385632Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9385947Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9386243Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9386629Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9386977Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9387296Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9387613Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9387900Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9388248Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9388583Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9388921Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9389257Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9389546Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9389914Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9390252Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9390578Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9390891Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9391187Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9391525Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9392506Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9392881Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9393216Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9393557Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9393901Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9394249Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9394571Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9394894Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9395186Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9395536Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9395875Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9396197Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9396538Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9396844Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9397194Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9397531Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9397860Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9398174Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9398470Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9398818Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9399150Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9399478Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9399818Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9400440Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9400793Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9401143Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9401468Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9401798Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9402094Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9402437Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9402788Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9403139Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9403454Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9403752Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9404096Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9404431Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9404745Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9405056Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9405332Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9405673Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9405999Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9406348Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9406654Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9406939Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9407277Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9407608Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9407929Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9408232Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9408517Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9408845Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9409198Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9409514Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9409844Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9410134Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9410466Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9411314Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9411649Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9411966Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9412249Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:07:46.9412593Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9412928Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9413292Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:46.9413607Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:46.9413889Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:07:46.9414233Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9414572Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9414892Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:46.9415194Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:46.9415482Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:07:46.9415822Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:46.9416215Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:46.9416568Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:46.9416871Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:46.9417221Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:07:46.9417535Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:07:46.9417848Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:07:46.9418210Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:07:46.9418570Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:07:46.9418913Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:07:46.9419257Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:07:46.9419368Z         
2025-03-14T05:07:46.9419655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9420130Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9420198Z         
2025-03-14T05:07:46.9420481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9421895Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9421973Z         
2025-03-14T05:07:46.9422260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:07:46.9422429Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:07:46.9422503Z         
2025-03-14T05:07:46.9422867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:07:46.9423466Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:07:46.9423532Z         
2025-03-14T05:07:46.9423798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9424261Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9424344Z         
2025-03-14T05:07:46.9424616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9426166Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9426245Z         
2025-03-14T05:07:46.9426579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9426732Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:07:46.9426799Z         
2025-03-14T05:07:46.9427073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9427519Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9427596Z         
2025-03-14T05:07:46.9427867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9429404Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9429492Z         
2025-03-14T05:07:46.9429801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9429954Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:07:46.9430037Z         
2025-03-14T05:07:46.9430303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9430789Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9430863Z         
2025-03-14T05:07:46.9431134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9432693Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9432770Z         
2025-03-14T05:07:46.9433028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9433520Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.9433589Z         
2025-03-14T05:07:46.9433861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9435460Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9435536Z         
2025-03-14T05:07:46.9435829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9435978Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:07:46.9436052Z         
2025-03-14T05:07:46.9436363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9436530Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:07:46.9436599Z         
2025-03-14T05:07:46.9436882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9437318Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9437395Z         
2025-03-14T05:07:46.9437663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9439220Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9439292Z         
2025-03-14T05:07:46.9439569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9439717Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:07:46.9439780Z         
2025-03-14T05:07:46.9440077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9440495Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9440569Z         
2025-03-14T05:07:46.9440827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9442306Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9442381Z         
2025-03-14T05:07:46.9442659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9442821Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:07:46.9442884Z         
2025-03-14T05:07:46.9443133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9443566Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9443639Z         
2025-03-14T05:07:46.9443896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9445395Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9445468Z         
2025-03-14T05:07:46.9445743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9445903Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:07:46.9445969Z         
2025-03-14T05:07:46.9446253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9446425Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:07:46.9446501Z         
2025-03-14T05:07:46.9446742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9447161Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9447226Z         
2025-03-14T05:07:46.9447487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9448968Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9449048Z         
2025-03-14T05:07:46.9449338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9449474Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:07:46.9449550Z         
2025-03-14T05:07:46.9449807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9450230Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9450293Z         
2025-03-14T05:07:46.9450556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9452061Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9452127Z         
2025-03-14T05:07:46.9452412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9452545Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:07:46.9452618Z         
2025-03-14T05:07:46.9452885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9453318Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9453383Z         
2025-03-14T05:07:46.9453648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9455129Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9455198Z         
2025-03-14T05:07:46.9455479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9455652Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:07:46.9455724Z         
2025-03-14T05:07:46.9456004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9456176Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:07:46.9456240Z         
2025-03-14T05:07:46.9456494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9456985Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9457052Z         
2025-03-14T05:07:46.9457320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9458819Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9458897Z         
2025-03-14T05:07:46.9459188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9459370Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:07:46.9459437Z         
2025-03-14T05:07:46.9459693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9460122Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9460187Z         
2025-03-14T05:07:46.9460457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9461959Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9462051Z         
2025-03-14T05:07:46.9462339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9462493Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:07:46.9462584Z         
2025-03-14T05:07:46.9462830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9463267Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9463334Z         
2025-03-14T05:07:46.9463603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9465174Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9465256Z         
2025-03-14T05:07:46.9465515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9465993Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.9466066Z         
2025-03-14T05:07:46.9466330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9467881Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9467959Z         
2025-03-14T05:07:46.9468242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9468401Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:07:46.9468466Z         
2025-03-14T05:07:46.9468756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9468923Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:07:46.9468997Z         
2025-03-14T05:07:46.9469246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9469720Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9469784Z         
2025-03-14T05:07:46.9470051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9471557Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9471634Z         
2025-03-14T05:07:46.9471926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9472066Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:07:46.9472138Z         
2025-03-14T05:07:46.9472418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9472851Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9472915Z         
2025-03-14T05:07:46.9473185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9474675Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9474752Z         
2025-03-14T05:07:46.9475046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9475184Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:07:46.9475275Z         
2025-03-14T05:07:46.9475526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9475964Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9476044Z         
2025-03-14T05:07:46.9476316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9478478Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9478564Z         
2025-03-14T05:07:46.9478848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9479000Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:07:46.9479075Z         
2025-03-14T05:07:46.9479348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9479538Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:07:46.9479604Z         
2025-03-14T05:07:46.9479853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9480260Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9480335Z         
2025-03-14T05:07:46.9480589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9482238Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9482319Z         
2025-03-14T05:07:46.9482659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9482811Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:07:46.9482878Z         
2025-03-14T05:07:46.9483157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9483580Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9483655Z         
2025-03-14T05:07:46.9483916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9485450Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9485526Z         
2025-03-14T05:07:46.9485804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9485951Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:07:46.9486014Z         
2025-03-14T05:07:46.9486310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9486721Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9486795Z         
2025-03-14T05:07:46.9487048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9488518Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9488589Z         
2025-03-14T05:07:46.9488861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9489033Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:07:46.9489096Z         
2025-03-14T05:07:46.9489381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9489551Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:07:46.9489624Z         
2025-03-14T05:07:46.9489870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9490284Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9490349Z         
2025-03-14T05:07:46.9490612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9492944Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9493038Z         
2025-03-14T05:07:46.9493358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9493557Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:07:46.9493636Z         
2025-03-14T05:07:46.9493893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9494317Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9494381Z         
2025-03-14T05:07:46.9494644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9496125Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9496207Z         
2025-03-14T05:07:46.9496490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9496628Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:07:46.9496715Z         
2025-03-14T05:07:46.9496955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9497374Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9497445Z         
2025-03-14T05:07:46.9497700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9499172Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9499239Z         
2025-03-14T05:07:46.9499515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9499664Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:07:46.9499737Z         
2025-03-14T05:07:46.9500034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9500188Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:07:46.9500250Z         
2025-03-14T05:07:46.9500500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9500910Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9500975Z         
2025-03-14T05:07:46.9501236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9502702Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9502790Z         
2025-03-14T05:07:46.9503069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9503225Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:07:46.9503288Z         
2025-03-14T05:07:46.9503537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9503949Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9504016Z         
2025-03-14T05:07:46.9504343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9505920Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9505996Z         
2025-03-14T05:07:46.9506285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9506453Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:07:46.9506528Z         
2025-03-14T05:07:46.9506778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9507204Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9507271Z         
2025-03-14T05:07:46.9507540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9509052Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9510762Z         
2025-03-14T05:07:46.9511114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9511864Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:46.9512463Z         
2025-03-14T05:07:46.9512822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9514712Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9516443Z         
2025-03-14T05:07:46.9516840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9517324Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:07:46.9517587Z         
2025-03-14T05:07:46.9517967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9518454Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:07:46.9518745Z         
2025-03-14T05:07:46.9519084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9519804Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9520337Z         
2025-03-14T05:07:46.9520685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9522569Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9525473Z         
2025-03-14T05:07:46.9525851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9526366Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:07:46.9526622Z         
2025-03-14T05:07:46.9526958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9527698Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9528230Z         
2025-03-14T05:07:46.9528577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9530379Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9531951Z         
2025-03-14T05:07:46.9532316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9532783Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:07:46.9533036Z         
2025-03-14T05:07:46.9533366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9534133Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9534661Z         
2025-03-14T05:07:46.9535214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9537804Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9539424Z         
2025-03-14T05:07:46.9539789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9540265Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:07:46.9540552Z         
2025-03-14T05:07:46.9540916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9541396Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:07:46.9541680Z         
2025-03-14T05:07:46.9542002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9542721Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9543243Z         
2025-03-14T05:07:46.9543590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9545584Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9547213Z         
2025-03-14T05:07:46.9547611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9548127Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:07:46.9548446Z         
2025-03-14T05:07:46.9548819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9549597Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9550167Z         
2025-03-14T05:07:46.9550541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9552483Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9554251Z         
2025-03-14T05:07:46.9554640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9555183Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:07:46.9555454Z         
2025-03-14T05:07:46.9555813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9556609Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9557154Z         
2025-03-14T05:07:46.9557509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9559363Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9560985Z         
2025-03-14T05:07:46.9561359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9561845Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:07:46.9562113Z         
2025-03-14T05:07:46.9562524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9563008Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:07:46.9563270Z         
2025-03-14T05:07:46.9563606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9564323Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9564859Z         
2025-03-14T05:07:46.9565215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9567053Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9568694Z         
2025-03-14T05:07:46.9569062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9569540Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:07:46.9569811Z         
2025-03-14T05:07:46.9570142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9570860Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9571389Z         
2025-03-14T05:07:46.9572197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9574361Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9575992Z         
2025-03-14T05:07:46.9576377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9576857Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:07:46.9577184Z         
2025-03-14T05:07:46.9577887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9578617Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9579158Z         
2025-03-14T05:07:46.9579504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9581311Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9583594Z         
2025-03-14T05:07:46.9583966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9584789Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:07:46.9585065Z         
2025-03-14T05:07:46.9585450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9585969Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:07:46.9586233Z         
2025-03-14T05:07:46.9586571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9587305Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9587853Z         
2025-03-14T05:07:46.9588208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9590054Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9591705Z         
2025-03-14T05:07:46.9592136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9592622Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:07:46.9592884Z         
2025-03-14T05:07:46.9593215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9593952Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9594494Z         
2025-03-14T05:07:46.9594849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9596680Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9598317Z         
2025-03-14T05:07:46.9598690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9599165Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:07:46.9599439Z         
2025-03-14T05:07:46.9599773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9600506Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9601050Z         
2025-03-14T05:07:46.9601394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9603176Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9604753Z         
2025-03-14T05:07:46.9605111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9605578Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:07:46.9605878Z         
2025-03-14T05:07:46.9606247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9606725Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:07:46.9606981Z         
2025-03-14T05:07:46.9607313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9608012Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9608542Z         
2025-03-14T05:07:46.9608893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9610695Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9612292Z         
2025-03-14T05:07:46.9612666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9613153Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:07:46.9613408Z         
2025-03-14T05:07:46.9613739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9614446Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9614979Z         
2025-03-14T05:07:46.9615319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9617099Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9618674Z         
2025-03-14T05:07:46.9619078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9619539Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:07:46.9619788Z         
2025-03-14T05:07:46.9620119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9620826Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9621362Z         
2025-03-14T05:07:46.9621734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9623502Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9625292Z         
2025-03-14T05:07:46.9625675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9626198Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:07:46.9626509Z         
2025-03-14T05:07:46.9626909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9627425Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:07:46.9627702Z         
2025-03-14T05:07:46.9628061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9628827Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9629391Z         
2025-03-14T05:07:46.9629775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9631724Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9633511Z         
2025-03-14T05:07:46.9633913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9634421Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:07:46.9634697Z         
2025-03-14T05:07:46.9635057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9635799Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9636341Z         
2025-03-14T05:07:46.9636695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9638544Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9640176Z         
2025-03-14T05:07:46.9640550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9641043Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:07:46.9641300Z         
2025-03-14T05:07:46.9641640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9642378Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9642927Z         
2025-03-14T05:07:46.9643279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9645103Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9646719Z         
2025-03-14T05:07:46.9647118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9647596Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:07:46.9647858Z         
2025-03-14T05:07:46.9648217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9648685Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:07:46.9648938Z         
2025-03-14T05:07:46.9649268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9649973Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9650491Z         
2025-03-14T05:07:46.9650833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9652607Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9654222Z         
2025-03-14T05:07:46.9654592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9655059Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:07:46.9655312Z         
2025-03-14T05:07:46.9655638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9656349Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9656876Z         
2025-03-14T05:07:46.9657225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9659016Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9660656Z         
2025-03-14T05:07:46.9661031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9661501Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:07:46.9661750Z         
2025-03-14T05:07:46.9662091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9662821Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9663374Z         
2025-03-14T05:07:46.9663731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9665824Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9667564Z         
2025-03-14T05:07:46.9667957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9668496Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:07:46.9668778Z         
2025-03-14T05:07:46.9669176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9669689Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:07:46.9669971Z         
2025-03-14T05:07:46.9670328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9671097Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9671663Z         
2025-03-14T05:07:46.9672040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9674042Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9675679Z         
2025-03-14T05:07:46.9676045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9676524Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:07:46.9676784Z         
2025-03-14T05:07:46.9677120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9677840Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9678378Z         
2025-03-14T05:07:46.9678729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9680554Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9682615Z         
2025-03-14T05:07:46.9682990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9683466Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:07:46.9683728Z         
2025-03-14T05:07:46.9684066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9684786Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9685321Z         
2025-03-14T05:07:46.9685668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9687455Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9689112Z         
2025-03-14T05:07:46.9689478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9689961Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:07:46.9690233Z         
2025-03-14T05:07:46.9690603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9691084Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:07:46.9691354Z         
2025-03-14T05:07:46.9691690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9692416Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9692955Z         
2025-03-14T05:07:46.9693314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9695123Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9696734Z         
2025-03-14T05:07:46.9697098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9697567Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:07:46.9697818Z         
2025-03-14T05:07:46.9698149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9698868Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9699396Z         
2025-03-14T05:07:46.9699740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9701561Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9703133Z         
2025-03-14T05:07:46.9703496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9703962Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:07:46.9704328Z         
2025-03-14T05:07:46.9704715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9705571Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9706137Z         
2025-03-14T05:07:46.9706491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9708528Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9710421Z         
2025-03-14T05:07:46.9710832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9711385Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:07:46.9711686Z         
2025-03-14T05:07:46.9712099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9712639Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:07:46.9712922Z         
2025-03-14T05:07:46.9713278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9713997Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9714526Z         
2025-03-14T05:07:46.9714862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9716707Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9718290Z         
2025-03-14T05:07:46.9718653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9719127Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:07:46.9719385Z         
2025-03-14T05:07:46.9719715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9720434Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9720965Z         
2025-03-14T05:07:46.9721306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9723102Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9724705Z         
2025-03-14T05:07:46.9725070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9725539Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:07:46.9725791Z         
2025-03-14T05:07:46.9726123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9726838Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9727373Z         
2025-03-14T05:07:46.9727718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9729535Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9731128Z         
2025-03-14T05:07:46.9731482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9731957Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:07:46.9732220Z         
2025-03-14T05:07:46.9732578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9733052Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:07:46.9733309Z         
2025-03-14T05:07:46.9733642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9734347Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9734869Z         
2025-03-14T05:07:46.9735211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9736993Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9738638Z         
2025-03-14T05:07:46.9739008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9739483Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:07:46.9739754Z         
2025-03-14T05:07:46.9740089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9740808Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9741343Z         
2025-03-14T05:07:46.9741685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9744035Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9745764Z         
2025-03-14T05:07:46.9746147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9746627Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:07:46.9746891Z         
2025-03-14T05:07:46.9747236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9747987Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9748542Z         
2025-03-14T05:07:46.9748895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9750755Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9752411Z         
2025-03-14T05:07:46.9752779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9753264Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:07:46.9753536Z         
2025-03-14T05:07:46.9753907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9754388Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:07:46.9754650Z         
2025-03-14T05:07:46.9754984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9755706Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9756237Z         
2025-03-14T05:07:46.9756585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9758470Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9760098Z         
2025-03-14T05:07:46.9760463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9760928Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:07:46.9761183Z         
2025-03-14T05:07:46.9761512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9762222Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9762741Z         
2025-03-14T05:07:46.9763085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9764884Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9766490Z         
2025-03-14T05:07:46.9766852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9767320Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:07:46.9767574Z         
2025-03-14T05:07:46.9767905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9768624Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9769154Z         
2025-03-14T05:07:46.9769497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9771316Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9772900Z         
2025-03-14T05:07:46.9773253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9773724Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:07:46.9773986Z         
2025-03-14T05:07:46.9774342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9774811Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:07:46.9775066Z         
2025-03-14T05:07:46.9775396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9776101Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9776640Z         
2025-03-14T05:07:46.9776982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9778782Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9780365Z         
2025-03-14T05:07:46.9780748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9781220Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:07:46.9781597Z         
2025-03-14T05:07:46.9781949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9782685Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9783233Z         
2025-03-14T05:07:46.9783587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9785610Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9787240Z         
2025-03-14T05:07:46.9787614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9788093Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:07:46.9788354Z         
2025-03-14T05:07:46.9788693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9789437Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9789983Z         
2025-03-14T05:07:46.9790334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9792239Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9793888Z         
2025-03-14T05:07:46.9794256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9794749Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:07:46.9795021Z         
2025-03-14T05:07:46.9795395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9795881Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:07:46.9796145Z         
2025-03-14T05:07:46.9796482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9797210Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9797744Z         
2025-03-14T05:07:46.9798124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9800003Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9801582Z         
2025-03-14T05:07:46.9801951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9802423Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:07:46.9802676Z         
2025-03-14T05:07:46.9803005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9803718Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9804266Z         
2025-03-14T05:07:46.9804608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9806403Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9808018Z         
2025-03-14T05:07:46.9808383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9808849Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:07:46.9809104Z         
2025-03-14T05:07:46.9809432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9810144Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9810673Z         
2025-03-14T05:07:46.9811018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9812836Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9814424Z         
2025-03-14T05:07:46.9814782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9815258Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:07:46.9815523Z         
2025-03-14T05:07:46.9815885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9816362Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:07:46.9816622Z         
2025-03-14T05:07:46.9816954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9817659Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9818193Z         
2025-03-14T05:07:46.9818545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9820350Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9821934Z         
2025-03-14T05:07:46.9822300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9822764Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:07:46.9823016Z         
2025-03-14T05:07:46.9823345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9824063Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9824662Z         
2025-03-14T05:07:46.9825051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9826902Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9828533Z         
2025-03-14T05:07:46.9828913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9829402Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:07:46.9829662Z         
2025-03-14T05:07:46.9830000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9830738Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9831297Z         
2025-03-14T05:07:46.9831651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9833499Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9835128Z         
2025-03-14T05:07:46.9835498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9835981Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:07:46.9836242Z         
2025-03-14T05:07:46.9836609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9837088Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:07:46.9837348Z         
2025-03-14T05:07:46.9837683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9838468Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9838987Z         
2025-03-14T05:07:46.9839331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9841117Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9842688Z         
2025-03-14T05:07:46.9843049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9843922Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:07:46.9844182Z         
2025-03-14T05:07:46.9844515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9845258Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9845796Z         
2025-03-14T05:07:46.9846160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9847973Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9849552Z         
2025-03-14T05:07:46.9849916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9850384Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:07:46.9850637Z         
2025-03-14T05:07:46.9850965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9851689Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9852214Z         
2025-03-14T05:07:46.9852592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9854401Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9855986Z         
2025-03-14T05:07:46.9856343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9856814Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:07:46.9857087Z         
2025-03-14T05:07:46.9857447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9857916Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:07:46.9858188Z         
2025-03-14T05:07:46.9858515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9859221Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9859763Z         
2025-03-14T05:07:46.9860104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9861905Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9863506Z         
2025-03-14T05:07:46.9863881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9864427Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:07:46.9864500Z         
2025-03-14T05:07:46.9864754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9865255Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9865325Z         
2025-03-14T05:07:46.9865599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9867126Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9867195Z         
2025-03-14T05:07:46.9867485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9867620Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:07:46.9867693Z         
2025-03-14T05:07:46.9867939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9868406Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9868489Z         
2025-03-14T05:07:46.9868758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9870268Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9870336Z         
2025-03-14T05:07:46.9870620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9870768Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:07:46.9870839Z         
2025-03-14T05:07:46.9871116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9871264Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:07:46.9871329Z         
2025-03-14T05:07:46.9871633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9872043Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9872114Z         
2025-03-14T05:07:46.9872375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9873889Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9873963Z         
2025-03-14T05:07:46.9874244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9874391Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:07:46.9874473Z         
2025-03-14T05:07:46.9874727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9875154Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9875251Z         
2025-03-14T05:07:46.9875514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9877035Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9877112Z         
2025-03-14T05:07:46.9877395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9877545Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:07:46.9877611Z         
2025-03-14T05:07:46.9877866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9878335Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9878410Z         
2025-03-14T05:07:46.9878676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9880146Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9880220Z         
2025-03-14T05:07:46.9880489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9880647Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:07:46.9880709Z         
2025-03-14T05:07:46.9881004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9881146Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:07:46.9881218Z         
2025-03-14T05:07:46.9881619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9882044Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9882116Z         
2025-03-14T05:07:46.9882379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9883868Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9883933Z         
2025-03-14T05:07:46.9884224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9884370Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:07:46.9884434Z         
2025-03-14T05:07:46.9884747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9885160Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9885230Z         
2025-03-14T05:07:46.9885485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9886958Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9887030Z         
2025-03-14T05:07:46.9887301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9887462Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:07:46.9887525Z         
2025-03-14T05:07:46.9887779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9888225Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9888296Z         
2025-03-14T05:07:46.9888550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9890024Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9890097Z         
2025-03-14T05:07:46.9890367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9890528Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:07:46.9890594Z         
2025-03-14T05:07:46.9890876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9891045Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:07:46.9891120Z         
2025-03-14T05:07:46.9891362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9891776Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9891839Z         
2025-03-14T05:07:46.9892106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9893592Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9893680Z         
2025-03-14T05:07:46.9893965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9894100Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:07:46.9894183Z         
2025-03-14T05:07:46.9894423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9894847Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9894910Z         
2025-03-14T05:07:46.9895175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9896637Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9896710Z         
2025-03-14T05:07:46.9896997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9897127Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:07:46.9897227Z         
2025-03-14T05:07:46.9897472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9897894Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9897958Z         
2025-03-14T05:07:46.9898222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9899710Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9899775Z         
2025-03-14T05:07:46.9900051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9900233Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:07:46.9900304Z         
2025-03-14T05:07:46.9900585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9900747Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:07:46.9900809Z         
2025-03-14T05:07:46.9901059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9901469Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9901542Z         
2025-03-14T05:07:46.9901797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9903290Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9903366Z         
2025-03-14T05:07:46.9903673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9903818Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:07:46.9903885Z         
2025-03-14T05:07:46.9904185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9904617Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9904694Z         
2025-03-14T05:07:46.9904963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9906506Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9906599Z         
2025-03-14T05:07:46.9906887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9907034Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:07:46.9907115Z         
2025-03-14T05:07:46.9907377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9907801Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9907875Z         
2025-03-14T05:07:46.9908145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9909655Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9909732Z         
2025-03-14T05:07:46.9910015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9910208Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:07:46.9910276Z         
2025-03-14T05:07:46.9910565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9910708Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:07:46.9910781Z         
2025-03-14T05:07:46.9911030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9911506Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:46.9911572Z         
2025-03-14T05:07:46.9911846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9913375Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9913460Z         
2025-03-14T05:07:46.9913767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9913904Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:07:46.9913975Z         
2025-03-14T05:07:46.9914226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9914661Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:46.9914734Z         
2025-03-14T05:07:46.9914998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9916518Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9916584Z         
2025-03-14T05:07:46.9916914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9917052Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:07:46.9917125Z         
2025-03-14T05:07:46.9917373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9917812Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:46.9917885Z         
2025-03-14T05:07:46.9918145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:46.9919685Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:46.9919770Z         
2025-03-14T05:07:46.9920062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:46.9920243Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:07:46.9920309Z         
2025-03-14T05:07:46.9920596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:46.9920737Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:07:46.9920808Z         
2025-03-14T05:07:46.9921240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:46.9921396Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:07:46.9921461Z         
2025-03-14T05:07:46.9921756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9921893Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:46.9921962Z         
2025-03-14T05:07:46.9922383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:46.9922538Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:07:46.9922601Z         
2025-03-14T05:07:46.9922889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9923059Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:07:46.9923132Z         
2025-03-14T05:07:46.9923498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:07:46.9923678Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:07:46.9923776Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:07:46.9923900Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:07:46.9923964Z         
2025-03-14T05:07:46.9924292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:07:46.9924417Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:07:46.9924487Z         
2025-03-14T05:07:46.9924807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:07:46.9924931Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:07:46.9924994Z         
2025-03-14T05:07:46.9925371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:07:46.9925579Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:07:46.9925668Z         
2025-03-14T05:07:46.9926077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:07:46.9926210Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:07:46.9926640Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:07:46.9926769Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:07:46.9926890Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:07:46.9926952Z         
2025-03-14T05:07:46.9927252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:46.9927379Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T05:07:46.9927452Z         
2025-03-14T05:07:46.9927700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:46.9928459Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:07:46.9928523Z         
2025-03-14T05:07:46.9928796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:07:46.9928981Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T05:07:46.9929053Z         
2025-03-14T05:07:46.9929449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:07:46.9930282Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:07:46.9930348Z         
2025-03-14T05:07:46.9930705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:07:46.9931502Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:07:46.9931566Z         
2025-03-14T05:07:46.9931900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:07:46.9932062Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:07:46.9932206Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:07:46.9932268Z         
2025-03-14T05:07:46.9932682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:07:46.9932852Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T05:07:46.9933032Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:07:46.9933206Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:07:46.9933278Z         
2025-03-14T05:07:46.9933671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:46.9933879Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:07:46.9933943Z         
2025-03-14T05:07:46.9934373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:46.9934526Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:07:46.9934671Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:46.9934816Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:46.9934881Z         
2025-03-14T05:07:46.9935251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:46.9935443Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:07:46.9935515Z         
2025-03-14T05:07:46.9935818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:46.9935962Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:46.9936027Z         
2025-03-14T05:07:46.9936340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:46.9936467Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:46.9936598Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:46.9936737Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:46.9936806Z         
2025-03-14T05:07:46.9937116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:46.9937243Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:46.9937357Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:46.9937506Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:07:46.9937568Z         
2025-03-14T05:07:46.9937870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:46.9938005Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:46.9938102Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:46.9938222Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:07:46.9938307Z         
2025-03-14T05:07:46.9938608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:46.9938756Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:46.9938845Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:46.9938975Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:07:46.9939038Z         
2025-03-14T05:07:46.9939375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:46.9939524Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:46.9939647Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:46.9939712Z         
2025-03-14T05:07:46.9940010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:46.9940155Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:46.9940271Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:46.9940334Z         
2025-03-14T05:07:46.9940628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:46.9940777Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:46.9940893Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:07:46.9940956Z         
2025-03-14T05:07:46.9941292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:46.9941480Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:46.9941589Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:07:46.9941659Z         
2025-03-14T05:07:46.9941983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:46.9942127Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:46.9942192Z         
2025-03-14T05:07:46.9942518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:46.9942649Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:46.9942722Z         
2025-03-14T05:07:46.9943061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:46.9943204Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:46.9943325Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:07:46.9943480Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:46.9943636Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:07:46.9943709Z         
2025-03-14T05:07:46.9944051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:46.9944280Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:46.9944407Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:07:46.9944565Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:46.9944701Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:07:46.9944776Z         
2025-03-14T05:07:46.9945106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:46.9945234Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:46.9945399Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:46.9945538Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:07:46.9945604Z         
2025-03-14T05:07:46.9945934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:46.9946047Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:46.9946216Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:46.9946345Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:07:46.9946417Z         
2025-03-14T05:07:46.9946720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:46.9946821Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:46.9946989Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:46.9947064Z         
2025-03-14T05:07:46.9947365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:46.9947465Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:46.9947574Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:46.9947646Z         
2025-03-14T05:07:46.9947939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:46.9948060Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:46.9948183Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:46.9948254Z         
2025-03-14T05:07:46.9948551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:46.9948670Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:46.9948792Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:46.9948865Z         
2025-03-14T05:07:46.9949199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:46.9949405Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:46.9949488Z         
2025-03-14T05:07:46.9949821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:46.9950001Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:46.9950066Z         
2025-03-14T05:07:46.9950447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:46.9950616Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:46.9950687Z         
2025-03-14T05:07:46.9951153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:46.9951290Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:46.9951353Z         
2025-03-14T05:07:46.9951649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9951785Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:07:46.9951856Z         
2025-03-14T05:07:46.9952274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:46.9952392Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:07:46.9952494Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:46.9952616Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:46.9952679Z         
2025-03-14T05:07:46.9953160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:46.9953322Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:46.9953556Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:07:46.9953620Z         
2025-03-14T05:07:46.9954064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:46.9954228Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:46.9954299Z         
2025-03-14T05:07:46.9954588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:46.9954744Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:46.9954805Z         
2025-03-14T05:07:46.9955179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:46.9955320Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:46.9955394Z         
2025-03-14T05:07:46.9955681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:46.9955847Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:07:46.9955910Z         
2025-03-14T05:07:46.9956282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:46.9956440Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:46.9956503Z         
2025-03-14T05:07:46.9956970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:46.9957106Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:07:46.9957233Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:46.9957382Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:46.9957522Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:46.9957586Z         
2025-03-14T05:07:46.9957943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:46.9958056Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:46.9958128Z         
2025-03-14T05:07:46.9958141Z 
2025-03-14T05:07:46.9958243Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:47.0047629Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", 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"f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:07:47.0048298Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:07:47.0048582Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0048901Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0049225Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0049524Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0049822Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0050141Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0050488Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0050819Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0051141Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0051457Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0051740Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0052078Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0052404Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0052722Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0053042Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0053329Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0053674Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0054013Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0054332Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0054644Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0054949Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:47.0055291Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0055633Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0055957Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:47.0056309Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:47.0056593Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0056933Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0057265Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0057578Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0057890Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0058168Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0058507Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0058834Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0059168Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0059473Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0059776Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0060113Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0060441Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0060761Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0061067Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0061354Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0061685Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0062023Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0062341Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0062679Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0062964Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0063292Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0063629Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0063944Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0064311Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0064613Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0065037Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0065457Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0065876Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0066264Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0066569Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0066905Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0067290Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0067652Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0068026Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0068344Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0068732Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0069141Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0069536Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0069901Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0070217Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0070588Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0070959Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0071312Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0071669Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0071995Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:47.0072384Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0072799Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0073193Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:47.0074290Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:47.0074615Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0074993Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0075360Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0075710Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0076013Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0076299Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0076640Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0077002Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0077322Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0077622Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0077905Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0078235Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0078573Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0078886Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0079194Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0079478Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0079829Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0080166Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0080896Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0081227Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0081635Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0081993Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0082325Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0082648Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0082962Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0083240Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0083587Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0083973Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0084292Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0085085Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0085381Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0085713Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0086052Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0086372Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0086943Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0087237Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0087608Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0087950Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0088282Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0088592Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0088867Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0089205Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0089536Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0089844Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0090153Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0090428Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0090794Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0091125Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0091445Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0091761Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0092038Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0092380Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0092710Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0093023Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0093322Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0093620Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0093953Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0094307Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0094628Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0094933Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0095235Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:07:47.0095584Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0095931Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0096259Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:07:47.0096583Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:07:47.0096863Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0097235Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0097571Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0097880Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0098188Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0098468Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0098804Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0099129Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0099446Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0099772Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0100058Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0100414Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0100739Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0101056Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0101360Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0101646Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0101978Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0102317Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0102633Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0102953Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0103276Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0103622Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0103966Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0104344Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0104676Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0104970Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0105328Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0105669Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0105997Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0106339Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0106626Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0106993Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0107316Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0107638Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0107940Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0108229Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0108564Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0108896Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0109221Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0109572Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0109858Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0110185Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0110519Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0110834Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0111156Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0111439Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0111784Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0112123Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0112456Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0112778Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0113078Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0113428Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0113758Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0114086Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0114402Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0114694Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0115043Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0115378Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0115709Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0116047Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0116341Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0116689Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0117041Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0117359Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0117678Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0117971Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0118322Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0118662Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0119000Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0119320Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0119620Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0119966Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0120301Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0120634Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0120951Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0121236Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0121582Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0121916Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0122294Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0122609Z         l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0122903Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0123242Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0123589Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0123918Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0124228Z         l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0124520Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0124861Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0125218Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0125539Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0125872Z         l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0126160Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0126504Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0126848Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0127169Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0127485Z         l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0127768Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0128124Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0128459Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0128818Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0129141Z         l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0129428Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0129767Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0130092Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0130415Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0130719Z         l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0131004Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0131330Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0131688Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0132020Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0132338Z         l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0132623Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0132951Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0133289Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0133604Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0133915Z         l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0134190Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0134524Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0134879Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0135200Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0135508Z         l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0135783Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0136119Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0136452Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0136769Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0137068Z         l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0137373Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0137720Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0138055Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0138386Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0138687Z         l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0138971Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0139302Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0139635Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0139947Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0140271Z         l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0140552Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0140897Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0141262Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0141581Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0141898Z         l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0142181Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0142528Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0142863Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0143185Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0143493Z         l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0143784Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0144235Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0144622Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0144976Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0145313Z         l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0145612Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0145961Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0146318Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0146660Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0146998Z         l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0147313Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0147718Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0148088Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0148424Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0148760Z         l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0149065Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0149430Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0149782Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0150128Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0150463Z         l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0150778Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0151150Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0151523Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0151874Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0152206Z         l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0152522Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0152886Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0153266Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0153618Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0153949Z         l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0154290Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0154639Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0154987Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0155309Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0155634Z         l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0155925Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0156278Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0156629Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0156947Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0157282Z         l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0157569Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0157933Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0158279Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0158599Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0158909Z         l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0159204Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0159555Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0159892Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0160221Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0160572Z         l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0160860Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0161191Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0161528Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0161839Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0162153Z         l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0162439Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0162766Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0163103Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0163428Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0163741Z         l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0164032Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0164371Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0164695Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0165013Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0165322Z         l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0165600Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0165935Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0166260Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0166580Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0166917Z         l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0167213Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0167552Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0167899Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0168225Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0168542Z         l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0168832Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0169174Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0169521Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0169853Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0170189Z         l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0170470Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0170818Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0171166Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0171491Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0171810Z         l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0172095Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0172446Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0172773Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0173123Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0173432Z         l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0173725Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0174069Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0174416Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0174754Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0175068Z         l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0175359Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0175696Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0176103Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0176426Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0176760Z         l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0177051Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0177398Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0177757Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0178082Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0178415Z         l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0178702Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0179064Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0179443Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0179773Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0180131Z         l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0180414Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0180775Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0181109Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0181628Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0181952Z         l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0182249Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0182639Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0182989Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0183339Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0183652Z         l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0183952Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0184387Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0184744Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0185068Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0185387Z         l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0185683Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0186023Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0186414Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0186742Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0187065Z         l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0187352Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0187706Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0188043Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0188377Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0188698Z         l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0188999Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0189346Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0189701Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0190031Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0190343Z         l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0190638Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0190990Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0191329Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0191650Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0191954Z         l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0192237Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0192595Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0192934Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0193249Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0193562Z         l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0193842Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0194186Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0194524Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0194835Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0195143Z         l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0195448Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0195809Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0196142Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0196466Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0196777Z         l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0197069Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0197415Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0197746Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0198067Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0198371Z         l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0198687Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0199019Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0199357Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0199669Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0199979Z         l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0200267Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:07:47.0200596Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0200937Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0201249Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:07:47.0201579Z         l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:07:47.0201857Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:07:47.0202220Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0202550Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0202871Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:07:47.0203188Z         l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:07:47.0203472Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:07:47.0203814Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:07:47.0204148Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:07:47.0204470Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:07:47.0204809Z         l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:07:47.0205158Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:07:47.0205476Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:07:47.0205786Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:07:47.0206153Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:07:47.0206507Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:07:47.0206854Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:07:47.0207186Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:07:47.0207263Z         
2025-03-14T05:07:47.0207548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0208037Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0208120Z         
2025-03-14T05:07:47.0208402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0209834Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0209904Z         
2025-03-14T05:07:47.0210202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:07:47.0210349Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:07:47.0210425Z         
2025-03-14T05:07:47.0210789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:07:47.0211035Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:07:47.0211100Z         
2025-03-14T05:07:47.0211390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0211803Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0211874Z         
2025-03-14T05:07:47.0212136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0213626Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0213703Z         
2025-03-14T05:07:47.0213984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0214130Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:07:47.0214209Z         
2025-03-14T05:07:47.0214462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0214878Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0214967Z         
2025-03-14T05:07:47.0215225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0216701Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0216776Z         
2025-03-14T05:07:47.0217057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0217203Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:07:47.0217266Z         
2025-03-14T05:07:47.0217520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0217986Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0218060Z         
2025-03-14T05:07:47.0218317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0219831Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0219907Z         
2025-03-14T05:07:47.0220157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0220599Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:47.0220680Z         
2025-03-14T05:07:47.0220950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0222516Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0222600Z         
2025-03-14T05:07:47.0222892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0223041Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:07:47.0223113Z         
2025-03-14T05:07:47.0223396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0223556Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:07:47.0223620Z         
2025-03-14T05:07:47.0223878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0224413Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0224491Z         
2025-03-14T05:07:47.0224776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0226308Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0226385Z         
2025-03-14T05:07:47.0226666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0226817Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:07:47.0226881Z         
2025-03-14T05:07:47.0227140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0228090Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0228200Z         
2025-03-14T05:07:47.0228478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0230036Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0230114Z         
2025-03-14T05:07:47.0230405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0230558Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:07:47.0230625Z         
2025-03-14T05:07:47.0230885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0231319Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0231385Z         
2025-03-14T05:07:47.0231690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0233183Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0233255Z         
2025-03-14T05:07:47.0233534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0233686Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:07:47.0233757Z         
2025-03-14T05:07:47.0234029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0234180Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:07:47.0234243Z         
2025-03-14T05:07:47.0234508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0234916Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0235009Z         
2025-03-14T05:07:47.0235270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0236761Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0236834Z         
2025-03-14T05:07:47.0237108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0237252Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:07:47.0237316Z         
2025-03-14T05:07:47.0237567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0238005Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0238086Z         
2025-03-14T05:07:47.0238761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0240530Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0240618Z         
2025-03-14T05:07:47.0240906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0241053Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:07:47.0241119Z         
2025-03-14T05:07:47.0241380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0241835Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0241912Z         
2025-03-14T05:07:47.0242200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0243721Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0243797Z         
2025-03-14T05:07:47.0244068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0244224Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:07:47.0244287Z         
2025-03-14T05:07:47.0244570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0244719Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:07:47.0244792Z         
2025-03-14T05:07:47.0245033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0245483Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0245552Z         
2025-03-14T05:07:47.0245816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0247305Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0247370Z         
2025-03-14T05:07:47.0247669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0247810Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:07:47.0247898Z         
2025-03-14T05:07:47.0248142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0248570Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0248651Z         
2025-03-14T05:07:47.0248917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0250417Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0250483Z         
2025-03-14T05:07:47.0250769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0250912Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:07:47.0250982Z         
2025-03-14T05:07:47.0251228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0251688Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0251756Z         
2025-03-14T05:07:47.0252020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0253493Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0253560Z         
2025-03-14T05:07:47.0253810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0254238Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:47.0254326Z         
2025-03-14T05:07:47.0254582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0256112Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0256204Z         
2025-03-14T05:07:47.0256476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0256628Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:07:47.0256693Z         
2025-03-14T05:07:47.0256973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0257120Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:07:47.0257191Z         
2025-03-14T05:07:47.0257430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0257848Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0257948Z         
2025-03-14T05:07:47.0258211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0259715Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0259781Z         
2025-03-14T05:07:47.0260062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0260200Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:07:47.0260272Z         
2025-03-14T05:07:47.0260511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0260932Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0261010Z         
2025-03-14T05:07:47.0261276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0262771Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0262841Z         
2025-03-14T05:07:47.0263133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0263268Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:07:47.0263338Z         
2025-03-14T05:07:47.0263580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0264004Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0264071Z         
2025-03-14T05:07:47.0264426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0265952Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0266018Z         
2025-03-14T05:07:47.0266306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0266461Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:07:47.0266534Z         
2025-03-14T05:07:47.0266816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0266970Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:07:47.0267035Z         
2025-03-14T05:07:47.0267287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0267735Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0267824Z         
2025-03-14T05:07:47.0268095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0269605Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0269681Z         
2025-03-14T05:07:47.0269962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0270111Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:07:47.0270176Z         
2025-03-14T05:07:47.0270429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0270890Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0270959Z         
2025-03-14T05:07:47.0271227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0272724Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0272799Z         
2025-03-14T05:07:47.0273084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0273229Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:07:47.0273301Z         
2025-03-14T05:07:47.0273548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0273981Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0274064Z         
2025-03-14T05:07:47.0274338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0275872Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0275947Z         
2025-03-14T05:07:47.0276231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0276386Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:07:47.0276457Z         
2025-03-14T05:07:47.0276738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0276893Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:07:47.0276960Z         
2025-03-14T05:07:47.0277212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0277694Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0277772Z         
2025-03-14T05:07:47.0278037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0279556Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0279631Z         
2025-03-14T05:07:47.0279919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0280066Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:07:47.0280131Z         
2025-03-14T05:07:47.0280402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0280827Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0280913Z         
2025-03-14T05:07:47.0281176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0282868Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0282949Z         
2025-03-14T05:07:47.0283233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0283382Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:07:47.0283446Z         
2025-03-14T05:07:47.0283702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0284207Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0284285Z         
2025-03-14T05:07:47.0284552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0286056Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0286130Z         
2025-03-14T05:07:47.0286400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0286552Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:07:47.0286614Z         
2025-03-14T05:07:47.0286895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0287063Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:07:47.0287135Z         
2025-03-14T05:07:47.0287383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0287812Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0287874Z         
2025-03-14T05:07:47.0288136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0289595Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0289662Z         
2025-03-14T05:07:47.0289945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0290077Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:07:47.0290147Z         
2025-03-14T05:07:47.0290388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0290828Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0290894Z         
2025-03-14T05:07:47.0291154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0292608Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0292677Z         
2025-03-14T05:07:47.0292958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0293090Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:07:47.0293184Z         
2025-03-14T05:07:47.0293425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0293840Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0293919Z         
2025-03-14T05:07:47.0294182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0295657Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0295723Z         
2025-03-14T05:07:47.0295973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0296385Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:07:47.0296458Z         
2025-03-14T05:07:47.0296711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0298273Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0298350Z         
2025-03-14T05:07:47.0298631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0298776Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:07:47.0298839Z         
2025-03-14T05:07:47.0299120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0299259Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:07:47.0299330Z         
2025-03-14T05:07:47.0299571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0299995Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0300074Z         
2025-03-14T05:07:47.0300337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0301809Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0301874Z         
2025-03-14T05:07:47.0320467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0320891Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:07:47.0320976Z         
2025-03-14T05:07:47.0321289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0321757Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0321858Z         
2025-03-14T05:07:47.0322294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0323863Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0323946Z         
2025-03-14T05:07:47.0324273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0324417Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:07:47.0324495Z         
2025-03-14T05:07:47.0324767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0325220Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0325318Z         
2025-03-14T05:07:47.0325607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0327159Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0327235Z         
2025-03-14T05:07:47.0327528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0327690Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:07:47.0327758Z         
2025-03-14T05:07:47.0328052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0328199Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:07:47.0328275Z         
2025-03-14T05:07:47.0328529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0328992Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0329066Z         
2025-03-14T05:07:47.0329332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0330876Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0330946Z         
2025-03-14T05:07:47.0331238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0331375Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:07:47.0331445Z         
2025-03-14T05:07:47.0331697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0332156Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0332247Z         
2025-03-14T05:07:47.0332514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0334049Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0334116Z         
2025-03-14T05:07:47.0334410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0334544Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:07:47.0334617Z         
2025-03-14T05:07:47.0334865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0335300Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0335374Z         
2025-03-14T05:07:47.0335679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0337197Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0337265Z         
2025-03-14T05:07:47.0337553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0337711Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:07:47.0337778Z         
2025-03-14T05:07:47.0338070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0338213Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:07:47.0338304Z         
2025-03-14T05:07:47.0338554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0338976Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0339056Z         
2025-03-14T05:07:47.0339329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0340823Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0340898Z         
2025-03-14T05:07:47.0341184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0341316Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:07:47.0341389Z         
2025-03-14T05:07:47.0341629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0342069Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0342136Z         
2025-03-14T05:07:47.0342401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0343919Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0344001Z         
2025-03-14T05:07:47.0344411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0344557Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:07:47.0344637Z         
2025-03-14T05:07:47.0344903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0345387Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0345468Z         
2025-03-14T05:07:47.0345732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0347222Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0347301Z         
2025-03-14T05:07:47.0347594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0347741Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:07:47.0347814Z         
2025-03-14T05:07:47.0348095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0348246Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:07:47.0348311Z         
2025-03-14T05:07:47.0348565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0348998Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0349073Z         
2025-03-14T05:07:47.0349336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0350826Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0350902Z         
2025-03-14T05:07:47.0351177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0351314Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:07:47.0351393Z         
2025-03-14T05:07:47.0351644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0352046Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0352135Z         
2025-03-14T05:07:47.0352390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0353856Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0353930Z         
2025-03-14T05:07:47.0354206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0354344Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:07:47.0354407Z         
2025-03-14T05:07:47.0354652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0355088Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0355163Z         
2025-03-14T05:07:47.0355418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0356885Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0356961Z         
2025-03-14T05:07:47.0357234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0357383Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:07:47.0357445Z         
2025-03-14T05:07:47.0357725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0357877Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:07:47.0357945Z         
2025-03-14T05:07:47.0358189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0358627Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0358692Z         
2025-03-14T05:07:47.0358958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0360427Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0360494Z         
2025-03-14T05:07:47.0360780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0360911Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:07:47.0360983Z         
2025-03-14T05:07:47.0361226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0361678Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0361744Z         
2025-03-14T05:07:47.0362009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0363470Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0363537Z         
2025-03-14T05:07:47.0363825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0363956Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:07:47.0364045Z         
2025-03-14T05:07:47.0364289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0364706Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0364784Z         
2025-03-14T05:07:47.0365046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0366505Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0366574Z         
2025-03-14T05:07:47.0366856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0366997Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:07:47.0367069Z         
2025-03-14T05:07:47.0367343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0367487Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:07:47.0367550Z         
2025-03-14T05:07:47.0367827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0368228Z         x_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0368299Z         
2025-03-14T05:07:47.0368559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0370019Z         x_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0370093Z         
2025-03-14T05:07:47.0370372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0370527Z         out_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:07:47.0370590Z         
2025-03-14T05:07:47.0370842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0371265Z         x_90: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0371337Z         
2025-03-14T05:07:47.0371592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0373062Z         x_91: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0373135Z         
2025-03-14T05:07:47.0373413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0373550Z         out_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:07:47.0373613Z         
2025-03-14T05:07:47.0373861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0374295Z         x_92: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0374367Z         
2025-03-14T05:07:47.0374631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0376104Z         x_93: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0376177Z         
2025-03-14T05:07:47.0376450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0376596Z         x_93 += out_51;  out_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:07:47.0376691Z         
2025-03-14T05:07:47.0376965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0377109Z         out_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:07:47.0377188Z         
2025-03-14T05:07:47.0377438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0377834Z         x_94: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0377904Z         
2025-03-14T05:07:47.0378158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0379624Z         x_95: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0379696Z         
2025-03-14T05:07:47.0379972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0380108Z         out_56: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:07:47.0380173Z         
2025-03-14T05:07:47.0380456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0380861Z         x_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0380931Z         
2025-03-14T05:07:47.0381190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0382951Z         x_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0383029Z         
2025-03-14T05:07:47.0383307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0383503Z         out_57: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:07:47.0383566Z         
2025-03-14T05:07:47.0383819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0384308Z         x_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0384388Z         
2025-03-14T05:07:47.0384653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0386157Z         x_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0386233Z         
2025-03-14T05:07:47.0386513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0386667Z         x_99 += out_55;  out_58: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:07:47.0386733Z         
2025-03-14T05:07:47.0387017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0387203Z         out_59: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:07:47.0387280Z         
2025-03-14T05:07:47.0387528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0387958Z         x_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0388021Z         
2025-03-14T05:07:47.0388282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0389758Z         x_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0389847Z         
2025-03-14T05:07:47.0390131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0390273Z         out_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:07:47.0390360Z         
2025-03-14T05:07:47.0390611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0391036Z         x_102: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0391101Z         
2025-03-14T05:07:47.0391372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0392888Z         x_103: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0392962Z         
2025-03-14T05:07:47.0393255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0393391Z         out_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:07:47.0393465Z         
2025-03-14T05:07:47.0393753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0394184Z         x_104: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0394249Z         
2025-03-14T05:07:47.0394519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0396035Z         x_105: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0396103Z         
2025-03-14T05:07:47.0396395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0396561Z         x_105 += out_59;  out_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:07:47.0396634Z         
2025-03-14T05:07:47.0396918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0397081Z         out_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:07:47.0397147Z         
2025-03-14T05:07:47.0397406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0397819Z         x_106: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0397896Z         
2025-03-14T05:07:47.0398160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0399691Z         x_107: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0399767Z         
2025-03-14T05:07:47.0400114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0400265Z         out_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:07:47.0400339Z         
2025-03-14T05:07:47.0400585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0400990Z         x_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0401062Z         
2025-03-14T05:07:47.0401318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0402781Z         x_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0402870Z         
2025-03-14T05:07:47.0403148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0403289Z         out_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:07:47.0403368Z         
2025-03-14T05:07:47.0403621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0404028Z         x_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0404100Z         
2025-03-14T05:07:47.0404354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0405839Z         x_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0405911Z         
2025-03-14T05:07:47.0406188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0406345Z         x_111 += out_63;  out_66: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:07:47.0406437Z         
2025-03-14T05:07:47.0406721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0406858Z         out_67: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:07:47.0406932Z         
2025-03-14T05:07:47.0407171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0407584Z         x_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0407648Z         
2025-03-14T05:07:47.0407915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0409402Z         x_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0409490Z         
2025-03-14T05:07:47.0409776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0409923Z         out_68: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:07:47.0409994Z         
2025-03-14T05:07:47.0410235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0410648Z         x_114: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0410721Z         
2025-03-14T05:07:47.0410976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0412450Z         x_115: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0412520Z         
2025-03-14T05:07:47.0412862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0412997Z         out_69: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:07:47.0413068Z         
2025-03-14T05:07:47.0413309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0413732Z         x_116: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0413804Z         
2025-03-14T05:07:47.0414057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0415528Z         x_117: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0415608Z         
2025-03-14T05:07:47.0415890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0416036Z         x_117 += out_67;  out_70: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:07:47.0416123Z         
2025-03-14T05:07:47.0416404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0416540Z         out_71: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:07:47.0416611Z         
2025-03-14T05:07:47.0416854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0417270Z         x_118: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0417333Z         
2025-03-14T05:07:47.0417600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0419091Z         x_119: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0419165Z         
2025-03-14T05:07:47.0419450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0419581Z         out_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:07:47.0419653Z         
2025-03-14T05:07:47.0419894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0420311Z         x_120: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0420375Z         
2025-03-14T05:07:47.0420639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0422116Z         x_121: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0422210Z         
2025-03-14T05:07:47.0422507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0422637Z         out_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:07:47.0422709Z         
2025-03-14T05:07:47.0422948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0423365Z         x_122: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0423430Z         
2025-03-14T05:07:47.0423698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0425275Z         x_123: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0425355Z         
2025-03-14T05:07:47.0425671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0425826Z         x_123 += out_71;  out_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:07:47.0425901Z         
2025-03-14T05:07:47.0426187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0426335Z         out_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:07:47.0426400Z         
2025-03-14T05:07:47.0426658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0427074Z         x_124: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0427148Z         
2025-03-14T05:07:47.0427416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0428932Z         x_125: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0429036Z         
2025-03-14T05:07:47.0429319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0429460Z         out_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:07:47.0429523Z         
2025-03-14T05:07:47.0429779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0430199Z         x_126: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0430275Z         
2025-03-14T05:07:47.0430536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0432091Z         x_127: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0432169Z         
2025-03-14T05:07:47.0432456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0432603Z         out_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:07:47.0432667Z         
2025-03-14T05:07:47.0432923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0433347Z         x_128: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0433424Z         
2025-03-14T05:07:47.0433691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0435219Z         x_129: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0435324Z         
2025-03-14T05:07:47.0435603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0435759Z         x_129 += out_75;  out_78: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:07:47.0435823Z         
2025-03-14T05:07:47.0436110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0436250Z         out_79: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:07:47.0436323Z         
2025-03-14T05:07:47.0436569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0436999Z         x_130: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0437065Z         
2025-03-14T05:07:47.0437337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0438894Z         x_131: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0438963Z         
2025-03-14T05:07:47.0439252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0439385Z         out_80: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:07:47.0439457Z         
2025-03-14T05:07:47.0439703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0440134Z         x_132: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0440200Z         
2025-03-14T05:07:47.0440467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0441986Z         x_133: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0442080Z         
2025-03-14T05:07:47.0442378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0442509Z         out_81: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:07:47.0442579Z         
2025-03-14T05:07:47.0442819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0443237Z         x_134: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0443302Z         
2025-03-14T05:07:47.0443562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0445072Z         x_135: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0445139Z         
2025-03-14T05:07:47.0445416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0445559Z         x_135 += out_79;  out_82: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:07:47.0445632Z         
2025-03-14T05:07:47.0445904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0446048Z         out_83: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:07:47.0446110Z         
2025-03-14T05:07:47.0446364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0446772Z         x_136: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0446838Z         
2025-03-14T05:07:47.0447098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0448560Z         x_137: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0448660Z         
2025-03-14T05:07:47.0448937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0449078Z         out_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:07:47.0449142Z         
2025-03-14T05:07:47.0449393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0449809Z         x_138: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0449875Z         
2025-03-14T05:07:47.0450140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0451652Z         x_139: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0451729Z         
2025-03-14T05:07:47.0452007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0452145Z         out_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:07:47.0452209Z         
2025-03-14T05:07:47.0452460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0452879Z         x_140: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0452942Z         
2025-03-14T05:07:47.0453205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0454681Z         x_141: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0454780Z         
2025-03-14T05:07:47.0455056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0455202Z         x_141 += out_83;  out_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:07:47.0455275Z         
2025-03-14T05:07:47.0455550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0455693Z         out_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:07:47.0455761Z         
2025-03-14T05:07:47.0456011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0456412Z         x_142: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0456482Z         
2025-03-14T05:07:47.0456736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0458269Z         x_143: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0458342Z         
2025-03-14T05:07:47.0458621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0458761Z         out_88: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:07:47.0458826Z         
2025-03-14T05:07:47.0459079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0459490Z         x_144: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0459561Z         
2025-03-14T05:07:47.0459817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0461291Z         x_145: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0461412Z         
2025-03-14T05:07:47.0461693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0461833Z         out_89: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:07:47.0461895Z         
2025-03-14T05:07:47.0462151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0462561Z         x_146: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0462634Z         
2025-03-14T05:07:47.0462893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0464472Z         x_147: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0464557Z         
2025-03-14T05:07:47.0464843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0465000Z         x_147 += out_87;  out_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:07:47.0465068Z         
2025-03-14T05:07:47.0465363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0465506Z         out_91: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:07:47.0465579Z         
2025-03-14T05:07:47.0465852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0466275Z         x_148: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0466342Z         
2025-03-14T05:07:47.0466637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0468153Z         x_149: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0468233Z         
2025-03-14T05:07:47.0468526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0468665Z         out_92: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:07:47.0468738Z         
2025-03-14T05:07:47.0468990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0469416Z         x_150: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0469479Z         
2025-03-14T05:07:47.0469750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0471292Z         x_151: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0471360Z         
2025-03-14T05:07:47.0471654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0471786Z         out_93: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:07:47.0471857Z         
2025-03-14T05:07:47.0472110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0472533Z         x_152: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0472597Z         
2025-03-14T05:07:47.0472865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0474416Z         x_153: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0474498Z         
2025-03-14T05:07:47.0474785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0474934Z         x_153 += out_91;  out_94: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:07:47.0475009Z         
2025-03-14T05:07:47.0475292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0475440Z         out_95: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:07:47.0475506Z         
2025-03-14T05:07:47.0475760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0476173Z         x_154: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0476251Z         
2025-03-14T05:07:47.0476510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0478074Z         x_155: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0478153Z         
2025-03-14T05:07:47.0478436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0478582Z         out_96: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:07:47.0478648Z         
2025-03-14T05:07:47.0478903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0479321Z         x_156: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0479410Z         
2025-03-14T05:07:47.0479671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0481156Z         x_157: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0481255Z         
2025-03-14T05:07:47.0481705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0481861Z         out_97: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:07:47.0481928Z         
2025-03-14T05:07:47.0482183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0482599Z         x_158: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0482673Z         
2025-03-14T05:07:47.0482944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0484490Z         x_159: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0484569Z         
2025-03-14T05:07:47.0484849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0485005Z         x_159 += out_95;  out_98: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:07:47.0485072Z         
2025-03-14T05:07:47.0485354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0485491Z         out_99: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:07:47.0485566Z         
2025-03-14T05:07:47.0485807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0486217Z         x_160: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0486308Z         
2025-03-14T05:07:47.0487304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0488869Z         x_161: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0488935Z         
2025-03-14T05:07:47.0489227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0489374Z         out_100: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:07:47.0489438Z         
2025-03-14T05:07:47.0489688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0490100Z         x_162: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0490174Z         
2025-03-14T05:07:47.0490460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0491940Z         x_163: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0492014Z         
2025-03-14T05:07:47.0492294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0492439Z         out_101: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:07:47.0492502Z         
2025-03-14T05:07:47.0492751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0493173Z         x_164: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0493258Z         
2025-03-14T05:07:47.0493515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0494999Z         x_165: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0495086Z         
2025-03-14T05:07:47.0495359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0495516Z         x_165 += out_99;  out_102: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:07:47.0495579Z         
2025-03-14T05:07:47.0495860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0496006Z         out_103: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:07:47.0496077Z         
2025-03-14T05:07:47.0496316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0496760Z         x_166: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0496828Z         
2025-03-14T05:07:47.0497091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0498559Z         x_167: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0498635Z         
2025-03-14T05:07:47.0498916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0499050Z         out_104: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:07:47.0499121Z         
2025-03-14T05:07:47.0499360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0499779Z         x_168: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0499859Z         
2025-03-14T05:07:47.0500122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0501651Z         x_169: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0501726Z         
2025-03-14T05:07:47.0502012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0502146Z         out_105: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:07:47.0502217Z         
2025-03-14T05:07:47.0502458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0502886Z         x_170: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0502952Z         
2025-03-14T05:07:47.0503243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0504861Z         x_171: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0504946Z         
2025-03-14T05:07:47.0505255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0505433Z         x_171 += out_103;  out_106: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:07:47.0505506Z         
2025-03-14T05:07:47.0505783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0505933Z         out_107: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:07:47.0506025Z         
2025-03-14T05:07:47.0506281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0506699Z         x_172: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0506790Z         
2025-03-14T05:07:47.0507047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0508527Z         x_173: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0508601Z         
2025-03-14T05:07:47.0508877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0509016Z         out_108: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:07:47.0509080Z         
2025-03-14T05:07:47.0509326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0509772Z         x_174: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0509845Z         
2025-03-14T05:07:47.0510105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0511604Z         x_175: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0511680Z         
2025-03-14T05:07:47.0511960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0512101Z         out_109: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:07:47.0512164Z         
2025-03-14T05:07:47.0512416Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0512848Z         x_176: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0512937Z         
2025-03-14T05:07:47.0513193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0514682Z         x_177: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0514755Z         
2025-03-14T05:07:47.0515025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0515185Z         x_177 += out_107;  out_110: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:07:47.0515250Z         
2025-03-14T05:07:47.0515530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0515673Z         out_111: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:07:47.0515747Z         
2025-03-14T05:07:47.0516016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0516435Z         x_178: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0516508Z         
2025-03-14T05:07:47.0516763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0518241Z         x_179: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0518308Z         
2025-03-14T05:07:47.0518591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0518723Z         out_112: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:07:47.0518813Z         
2025-03-14T05:07:47.0519054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0519476Z         x_180: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0519565Z         
2025-03-14T05:07:47.0519825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0521318Z         x_181: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0521384Z         
2025-03-14T05:07:47.0521671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0521803Z         out_113: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:07:47.0521873Z         
2025-03-14T05:07:47.0522119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0522562Z         x_182: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0522636Z         
2025-03-14T05:07:47.0522892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0524384Z         x_183: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0524451Z         
2025-03-14T05:07:47.0524727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0524886Z         x_183 += out_111;  out_114: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:07:47.0524950Z         
2025-03-14T05:07:47.0525251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0525392Z         out_115: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:07:47.0525462Z         
2025-03-14T05:07:47.0525707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0526132Z         x_184: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:07:47.0526197Z         
2025-03-14T05:07:47.0526459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0527940Z         x_185: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0528015Z         
2025-03-14T05:07:47.0528299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0528435Z         out_116: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:07:47.0528505Z         
2025-03-14T05:07:47.0528780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0529207Z         x_186: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:07:47.0529272Z         
2025-03-14T05:07:47.0529537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0531024Z         x_187: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0531100Z         
2025-03-14T05:07:47.0531385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0531533Z         out_117: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:07:47.0531605Z         
2025-03-14T05:07:47.0531850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0532297Z         x_188: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:07:47.0532362Z         
2025-03-14T05:07:47.0532628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:07:47.0534094Z         x_189: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:07:47.0534171Z         
2025-03-14T05:07:47.0534449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:07:47.0534601Z         x_189 += out_115;  out_118: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:07:47.0534674Z         
2025-03-14T05:07:47.0534949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:07:47.0535130Z         out_119: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:07:47.0535195Z         
2025-03-14T05:07:47.0535627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:47.0535778Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:07:47.0535850Z         
2025-03-14T05:07:47.0536136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:47.0536281Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:47.0536345Z         
2025-03-14T05:07:47.0536774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:47.0536923Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:07:47.0536997Z         
2025-03-14T05:07:47.0537279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:47.0537422Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:07:47.0537485Z         
2025-03-14T05:07:47.0537851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:07:47.0538042Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:07:47.0538152Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:07:47.0538286Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:07:47.0538358Z         
2025-03-14T05:07:47.0538679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:07:47.0538809Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:07:47.0538873Z         
2025-03-14T05:07:47.0539193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:07:47.0539310Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:07:47.0539382Z         
2025-03-14T05:07:47.0539751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:07:47.0539963Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:07:47.0540026Z         
2025-03-14T05:07:47.0540440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:07:47.0540571Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:07:47.0540985Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:07:47.0541111Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:07:47.0541255Z         x_190: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:07:47.0541326Z         
2025-03-14T05:07:47.0541617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:47.0541746Z         tensor: "f32[82125, 4][4, 1]cpu" = x_190.to(torch.float32);  x_190 = None
2025-03-14T05:07:47.0541809Z         
2025-03-14T05:07:47.0542064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:47.0542812Z         x_191: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_119, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_119 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:07:47.0542888Z         
2025-03-14T05:07:47.0543153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:07:47.0543343Z         x_192: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_191, inplace = False);  x_191 = None
2025-03-14T05:07:47.0543407Z         
2025-03-14T05:07:47.0543783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:07:47.0544700Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:07:47.0544790Z         
2025-03-14T05:07:47.0545162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:07:47.0545976Z         x_193: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_192, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_192 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:07:47.0546056Z         
2025-03-14T05:07:47.0546392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:07:47.0546556Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:07:47.0546698Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:07:47.0546777Z         
2025-03-14T05:07:47.0547193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:07:47.0547363Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_193.view(4, -1, 4, 73, 75);  x_193 = None
2025-03-14T05:07:47.0547580Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:07:47.0547760Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:07:47.0547832Z         
2025-03-14T05:07:47.0548232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:47.0548446Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:07:47.0548512Z         
2025-03-14T05:07:47.0548948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:47.0549098Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:07:47.0549257Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:47.0549394Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:47.0549470Z         
2025-03-14T05:07:47.0549839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:47.0550022Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:07:47.0550088Z         
2025-03-14T05:07:47.0550424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:47.0550564Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:47.0550636Z         
2025-03-14T05:07:47.0550972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:47.0551111Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:47.0551238Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:47.0551395Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:47.0551461Z         
2025-03-14T05:07:47.0551787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:47.0551911Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:47.0552039Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:47.0552186Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:07:47.0552263Z         
2025-03-14T05:07:47.0552574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:47.0552701Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:47.0552791Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:47.0552920Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:07:47.0552985Z         
2025-03-14T05:07:47.0553310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:47.0553451Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:47.0553586Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:47.0553715Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:07:47.0553788Z         
2025-03-14T05:07:47.0554115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:47.0554272Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:47.0554383Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:47.0554453Z         
2025-03-14T05:07:47.0554741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:47.0554898Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:47.0555018Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:47.0555083Z         
2025-03-14T05:07:47.0555376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:47.0555523Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:47.0555643Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:07:47.0555705Z         
2025-03-14T05:07:47.0556004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:47.0556201Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:47.0556318Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:07:47.0556382Z         
2025-03-14T05:07:47.0556718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:47.0556871Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:47.0556942Z         
2025-03-14T05:07:47.0557265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:47.0557402Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:47.0557466Z         
2025-03-14T05:07:47.0557809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:47.0557944Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:47.0558077Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:07:47.0558223Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:47.0558365Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:07:47.0558429Z         
2025-03-14T05:07:47.0558769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:47.0558903Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:47.0559031Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:07:47.0559176Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:47.0559348Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:07:47.0559415Z         
2025-03-14T05:07:47.0559746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:47.0559863Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:47.0560027Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:47.0560154Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:07:47.0560227Z         
2025-03-14T05:07:47.0560550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:47.0560671Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:47.0560831Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:47.0560968Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:07:47.0561029Z         
2025-03-14T05:07:47.0561339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:47.0561440Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:47.0561552Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:47.0561643Z         
2025-03-14T05:07:47.0561941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:47.0562042Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:47.0562155Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:47.0562240Z         
2025-03-14T05:07:47.0562541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:47.0562656Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:47.0562780Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:47.0562853Z         
2025-03-14T05:07:47.0563149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:47.0563267Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:47.0563387Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:47.0563460Z         
2025-03-14T05:07:47.0563801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:47.0563986Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:47.0564049Z         
2025-03-14T05:07:47.0564403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:47.0564564Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:47.0564641Z         
2025-03-14T05:07:47.0565030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:47.0565244Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:47.0565311Z         
2025-03-14T05:07:47.0565783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:47.0565915Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:47.0565988Z         
2025-03-14T05:07:47.0566270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:47.0566417Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:07:47.0566481Z         
2025-03-14T05:07:47.0566905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:47.0567019Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:07:47.0567129Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:47.0567241Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:47.0567313Z         
2025-03-14T05:07:47.0567758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:47.0567945Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:47.0568172Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:07:47.0568249Z         
2025-03-14T05:07:47.0568703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:47.0568873Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:47.0568955Z         
2025-03-14T05:07:47.0569241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:47.0569395Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:47.0569458Z         
2025-03-14T05:07:47.0569832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:47.0569977Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:47.0570049Z         
2025-03-14T05:07:47.0570336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:47.0570482Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:07:47.0570545Z         
2025-03-14T05:07:47.0570912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:47.0571050Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:47.0571120Z         
2025-03-14T05:07:47.0571614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:47.0571760Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:07:47.0571879Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:47.0572037Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:47.0572165Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:47.0572239Z         
2025-03-14T05:07:47.0572588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:47.0572712Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:47.0572775Z         
2025-03-14T05:07:55.7344532Z 
2025-03-14T05:07:55.7345302Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:55.7346873Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:07:55.7348664Z         l_features_res4_ = L_features_res4_
2025-03-14T05:07:55.7349176Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:07:55.7349835Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:07:55.7354512Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:07:55.7356410Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:07:55.7357133Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:07:55.7357984Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:07:55.7362536Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:07:55.7364300Z         
2025-03-14T05:07:55.7365045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:55.7370228Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:07:55.7374453Z         
2025-03-14T05:07:55.7379106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7381286Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:55.7381937Z         
2025-03-14T05:07:55.7382756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:55.7383442Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:07:55.7383723Z         
2025-03-14T05:07:55.7384213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7384732Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:07:55.7385006Z         
2025-03-14T05:07:55.7385483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:07:55.7386115Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:07:55.7386448Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:07:55.7386726Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:07:55.7386969Z         
2025-03-14T05:07:55.7387379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:07:55.7387887Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:07:55.7388132Z         
2025-03-14T05:07:55.7388544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:07:55.7389045Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:07:55.7389339Z         
2025-03-14T05:07:55.7389813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:07:55.7390464Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:07:55.7390824Z         
2025-03-14T05:07:55.7391365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:07:55.7391957Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:07:55.7392454Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:07:55.7392944Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:07:55.7393231Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:07:55.7393459Z         
2025-03-14T05:07:55.7393856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:55.7394324Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:07:55.7394561Z         
2025-03-14T05:07:55.7394895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:55.7395784Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:07:55.7396486Z         
2025-03-14T05:07:55.7396898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:07:55.7397429Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:07:55.7397733Z         
2025-03-14T05:07:55.7398205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:07:55.7399279Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:07:55.7400243Z         
2025-03-14T05:07:55.7400893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:07:55.7401902Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:07:55.7402922Z         
2025-03-14T05:07:55.7403511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:07:55.7404122Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:07:55.7404598Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:07:55.7404867Z         
2025-03-14T05:07:55.7405391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:07:55.7406011Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:07:55.7406388Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:07:55.7406788Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:07:55.7407082Z         
2025-03-14T05:07:55.7407572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:55.7408234Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:07:55.7408559Z         
2025-03-14T05:07:55.7409076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:55.7409705Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:07:55.7410056Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:55.7410400Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:55.7410661Z         
2025-03-14T05:07:55.7411127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:55.7411757Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:07:55.7412052Z         
2025-03-14T05:07:55.7412452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:55.7412955Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:55.7413213Z         
2025-03-14T05:07:55.7413609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:55.7414104Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:55.7414454Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:55.7414783Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:55.7415048Z         
2025-03-14T05:07:55.7415458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:55.7415956Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:55.7416259Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:55.7416583Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:07:55.7416850Z         
2025-03-14T05:07:55.7417247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:55.7417751Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:55.7418020Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:55.7418285Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:07:55.7418541Z         
2025-03-14T05:07:55.7418937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:55.7419446Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:55.7419734Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:55.7419996Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:07:55.7420239Z         
2025-03-14T05:07:55.7420657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:55.7421163Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:55.7421486Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:55.7421722Z         
2025-03-14T05:07:55.7422113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:55.7422620Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:55.7422939Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:55.7423175Z         
2025-03-14T05:07:55.7423573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:55.7424069Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:55.7424472Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:07:55.7424709Z         
2025-03-14T05:07:55.7425175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:55.7425734Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:55.7426102Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:07:55.7426348Z         
2025-03-14T05:07:55.7426778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:55.7427313Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:55.7427576Z         
2025-03-14T05:07:55.7428001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:55.7428540Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:55.7428800Z         
2025-03-14T05:07:55.7429231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:55.7429773Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:55.7430093Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:07:55.7430428Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:55.7430796Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:07:55.7431057Z         
2025-03-14T05:07:55.7431500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:55.7432059Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:55.7432379Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:07:55.7432711Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:55.7433055Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:07:55.7433312Z         
2025-03-14T05:07:55.7433731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:55.7434236Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:55.7434569Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:55.7434915Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:07:55.7435170Z         
2025-03-14T05:07:55.7435590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:55.7436086Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:55.7436421Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:55.7436777Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:07:55.7437036Z         
2025-03-14T05:07:55.7437435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:55.7437956Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:55.7438233Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:55.7438482Z         
2025-03-14T05:07:55.7438886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:55.7439353Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:55.7439624Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:55.7439868Z         
2025-03-14T05:07:55.7440269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:55.7440752Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:55.7441060Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:55.7441324Z         
2025-03-14T05:07:55.7441723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:55.7442207Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:55.7442504Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:55.7442762Z         
2025-03-14T05:07:55.7443203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:55.7443813Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:55.7444108Z         
2025-03-14T05:07:55.7444532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:55.7445091Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:55.7445377Z         
2025-03-14T05:07:55.7445841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:55.7446442Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:55.7446731Z         
2025-03-14T05:07:55.7447294Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:55.7447974Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:55.7448230Z         
2025-03-14T05:07:55.7448617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7449108Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:07:55.7449372Z         
2025-03-14T05:07:55.7449890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:55.7450489Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:07:55.7450776Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:55.7451049Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:55.7451280Z         
2025-03-14T05:07:55.7451863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:55.7452538Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:55.7452993Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:07:55.7453346Z         
2025-03-14T05:07:55.7453872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:55.7454527Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:55.7454804Z         
2025-03-14T05:07:55.7455182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7455679Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:55.7455948Z         
2025-03-14T05:07:55.7456409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:55.7456985Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:55.7457246Z         
2025-03-14T05:07:55.7457640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:55.7458131Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:07:55.7458394Z         
2025-03-14T05:07:55.7458850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:55.7459423Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:55.7459677Z         
2025-03-14T05:07:55.7460227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:55.7460873Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:07:55.7461181Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:55.7461508Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:55.7461843Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:55.7462110Z         
2025-03-14T05:07:55.7462569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:55.7463114Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:55.7463345Z         
2025-03-14T05:07:55.7463484Z 
2025-03-14T05:07:55.7463583Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:55.7465024Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:07:55.7466340Z         l_features_res4_ = L_features_res4_
2025-03-14T05:07:55.7466752Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:07:55.7467268Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:07:55.7467741Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:07:55.7468273Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:07:55.7468868Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:07:55.7469438Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:07:55.7469982Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:07:55.7470347Z         
2025-03-14T05:07:55.7470890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:55.7471549Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:07:55.7471817Z         
2025-03-14T05:07:55.7472215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7472749Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:55.7473003Z         
2025-03-14T05:07:55.7473521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:07:55.7474149Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:07:55.7474412Z         
2025-03-14T05:07:55.7474786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7475265Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:07:55.7475522Z         
2025-03-14T05:07:55.7475979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:07:55.7476582Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:07:55.7476914Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:07:55.7477187Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:07:55.7477426Z         
2025-03-14T05:07:55.7477841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:07:55.7478350Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:07:55.7478592Z         
2025-03-14T05:07:55.7479031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:07:55.7479532Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:07:55.7479775Z         
2025-03-14T05:07:55.7480235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:07:55.7480870Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:07:55.7481196Z         
2025-03-14T05:07:55.7481879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:07:55.7482481Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:07:55.7482986Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:07:55.7483459Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:07:55.7483740Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:07:55.7483966Z         
2025-03-14T05:07:55.7484343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:55.7485870Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:07:55.7486110Z         
2025-03-14T05:07:55.7486452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:07:55.7487352Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:07:55.7488072Z         
2025-03-14T05:07:55.7488432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:07:55.7488931Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:07:55.7489230Z         
2025-03-14T05:07:55.7489688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:07:55.7490724Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:07:55.7491442Z         
2025-03-14T05:07:55.7491876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:07:55.7492894Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:07:55.7493582Z         
2025-03-14T05:07:55.7494000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:07:55.7494531Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:07:55.7494864Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:07:55.7495115Z         
2025-03-14T05:07:55.7495609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:07:55.7496209Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:07:55.7496582Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:07:55.7496969Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:07:55.7497259Z         
2025-03-14T05:07:55.7497735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:55.7498380Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:07:55.7498694Z         
2025-03-14T05:07:55.7499214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:55.7499832Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:07:55.7500190Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:55.7500526Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:55.7500779Z         
2025-03-14T05:07:55.7501225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:55.7501801Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:07:55.7502087Z         
2025-03-14T05:07:55.7502475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:55.7502966Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:55.7503223Z         
2025-03-14T05:07:55.7503618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:55.7504105Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:55.7504474Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:55.7504798Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:55.7505066Z         
2025-03-14T05:07:55.7505482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:55.7505973Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:55.7506273Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:55.7506630Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:07:55.7506903Z         
2025-03-14T05:07:55.7507302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:55.7507803Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:55.7508992Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:55.7509266Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:07:55.7509515Z         
2025-03-14T05:07:55.7509915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:55.7510437Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:55.7510733Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:55.7511012Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:07:55.7511257Z         
2025-03-14T05:07:55.7511660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:55.7512162Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:55.7512485Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:55.7512719Z         
2025-03-14T05:07:55.7513098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:55.7513648Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:55.7513973Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:55.7514221Z         
2025-03-14T05:07:55.7514603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:55.7515111Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:55.7515431Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:07:55.7515669Z         
2025-03-14T05:07:55.7516056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:55.7516596Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:55.7516947Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:07:55.7517187Z         
2025-03-14T05:07:55.7517615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:55.7518152Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:55.7518412Z         
2025-03-14T05:07:55.7518831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:55.7519358Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:55.7519618Z         
2025-03-14T05:07:55.7520047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:55.7520614Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:55.7520930Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:07:55.7521255Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:55.7521593Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:07:55.7521848Z         
2025-03-14T05:07:55.7522270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:55.7522796Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:55.7523109Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:07:55.7523427Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:55.7523765Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:07:55.7524024Z         
2025-03-14T05:07:55.7524432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:55.7524924Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:55.7525243Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:55.7525578Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:07:55.7525852Z         
2025-03-14T05:07:55.7526251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:55.7526744Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:55.7527079Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:55.7527416Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:07:55.7527659Z         
2025-03-14T05:07:55.7528048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:55.7528496Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:55.7528753Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:55.7528985Z         
2025-03-14T05:07:55.7529367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:55.7529810Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:55.7530063Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:55.7530292Z         
2025-03-14T05:07:55.7530675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:55.7531135Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:55.7531420Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:55.7531668Z         
2025-03-14T05:07:55.7532589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:55.7533095Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:55.7533384Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:55.7533629Z         
2025-03-14T05:07:55.7534113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:55.7534681Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:55.7534968Z         
2025-03-14T05:07:55.7535374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:55.7535903Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:55.7536197Z         
2025-03-14T05:07:55.7536652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:55.7537237Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:55.7537521Z         
2025-03-14T05:07:55.7538078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:55.7538747Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:55.7538999Z         
2025-03-14T05:07:55.7539380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7539891Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:07:55.7540157Z         
2025-03-14T05:07:55.7540678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:55.7541296Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:07:55.7541569Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:55.7541846Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:55.7542076Z         
2025-03-14T05:07:55.7542615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:55.7543285Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:55.7543734Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:07:55.7544092Z         
2025-03-14T05:07:55.7544733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:55.7545433Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:55.7545734Z         
2025-03-14T05:07:55.7546121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:55.7546624Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:55.7546900Z         
2025-03-14T05:07:55.7547366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:55.7548001Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:55.7548267Z         
2025-03-14T05:07:55.7548653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:55.7549148Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:07:55.7549414Z         
2025-03-14T05:07:55.7549871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:55.7550441Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:55.7550704Z         
2025-03-14T05:07:55.7551277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:55.7551953Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:07:55.7552269Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:55.7552606Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:55.7552950Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:55.7553207Z         
2025-03-14T05:07:55.7553665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:55.7554213Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:55.7554444Z         
2025-03-14T05:07:56.3316829Z 
2025-03-14T05:07:56.3323285Z class GraphModule(torch.nn.Module):
2025-03-14T05:07:56.3324598Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T05:07:56.3325084Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:07:56.3325417Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:07:56.3325689Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:07:56.3325939Z         
2025-03-14T05:07:56.3326483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:07:56.3327179Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:07:56.3327516Z         
2025-03-14T05:07:56.3328056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:07:56.3328736Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:07:56.3329128Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:07:56.3329473Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:07:56.3329737Z         
2025-03-14T05:07:56.3330209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:07:56.3330802Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:07:56.3331224Z         
2025-03-14T05:07:56.3331656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:07:56.3332241Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:07:56.3332516Z         
2025-03-14T05:07:56.3332950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:07:56.3333507Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:07:56.3333830Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:56.3334173Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:07:56.3334431Z         
2025-03-14T05:07:56.3334832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:07:56.3335323Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:07:56.3335617Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:07:56.3335934Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:07:56.3336194Z         
2025-03-14T05:07:56.3336584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:07:56.3337106Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:07:56.3337368Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:07:56.3337625Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:07:56.3337862Z         
2025-03-14T05:07:56.3338258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:07:56.3338781Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:07:56.3339067Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:07:56.3339336Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:07:56.3339575Z         
2025-03-14T05:07:56.3339982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:07:56.3340478Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:07:56.3340797Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:07:56.3341028Z         
2025-03-14T05:07:56.3341409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:07:56.3341899Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:07:56.3342214Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:07:56.3342448Z         
2025-03-14T05:07:56.3342824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:07:56.3343309Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:07:56.3343629Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:07:56.3343862Z         
2025-03-14T05:07:56.3344533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:07:56.3345099Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:07:56.3345465Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:07:56.3345696Z         
2025-03-14T05:07:56.3346113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:07:56.3346638Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:07:56.3346892Z         
2025-03-14T05:07:56.3347301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:07:56.3347814Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:07:56.3348065Z         
2025-03-14T05:07:56.3348484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:07:56.3349014Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:07:56.3349322Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:07:56.3349651Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:07:56.3349994Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:07:56.3350267Z         
2025-03-14T05:07:56.3350693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:07:56.3351223Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:07:56.3351552Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:07:56.3351872Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:07:56.3352207Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:07:56.3352462Z         
2025-03-14T05:07:56.3352872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:07:56.3353358Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:07:56.3353675Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:07:56.3354012Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:07:56.3354260Z         
2025-03-14T05:07:56.3354669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:07:56.3355156Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:07:56.3355480Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:07:56.3355823Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:07:56.3356074Z         
2025-03-14T05:07:56.3356466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:07:56.3356916Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:07:56.3357205Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:07:56.3357440Z         
2025-03-14T05:07:56.3357828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:07:56.3358276Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:07:56.3358532Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:07:56.3358762Z         
2025-03-14T05:07:56.3359145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:07:56.3359607Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:07:56.3359896Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:07:56.3360141Z         
2025-03-14T05:07:56.3360522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:07:56.3360982Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:07:56.3361267Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:07:56.3361507Z         
2025-03-14T05:07:56.3361929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:07:56.3362499Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:07:56.3362808Z         
2025-03-14T05:07:56.3363220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:07:56.3363754Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:07:56.3364045Z         
2025-03-14T05:07:56.3364501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:07:56.3365081Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:07:56.3365363Z         
2025-03-14T05:07:56.3365916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:07:56.3366622Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:07:56.3366863Z         
2025-03-14T05:07:56.3367239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:56.3367712Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:07:56.3367952Z         
2025-03-14T05:07:56.3368461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:07:56.3369097Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:07:56.3369423Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:07:56.3369687Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:07:56.3370005Z         
2025-03-14T05:07:56.3370588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:07:56.3371257Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:07:56.3371703Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T05:07:56.3372050Z         
2025-03-14T05:07:56.3372579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:07:56.3373238Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:07:56.3373519Z         
2025-03-14T05:07:56.3373893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:07:56.3374386Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:07:56.3374654Z         
2025-03-14T05:07:56.3375116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:07:56.3375678Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:07:56.3375940Z         
2025-03-14T05:07:56.3376313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:07:56.3376819Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T05:07:56.3377075Z         
2025-03-14T05:07:56.3378675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:07:56.3379342Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:07:56.3379609Z         
2025-03-14T05:07:56.3380166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:07:56.3380826Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:07:56.3381130Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:07:56.3381661Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:07:56.3382011Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:07:56.3382264Z         
2025-03-14T05:07:56.3382719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:07:56.3383247Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:07:56.3383483Z         
2025-03-14T05:08:11.0574485Z 
2025-03-14T05:08:11.0575073Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:11.0577258Z     def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:11.0578728Z         l_stack0_ = L_stack0_
2025-03-14T05:08:11.0579142Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:08:11.0579738Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:08:11.0580328Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:08:11.0580915Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:08:11.0581424Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0582040Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0582465Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0582884Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0583202Z         
2025-03-14T05:08:11.0583799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:08:11.0584638Z         mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:08:11.0584928Z         
2025-03-14T05:08:11.0585366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:08:11.0586437Z         scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:08:11.0587197Z         
2025-03-14T05:08:11.0587629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:08:11.0588689Z         proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:08:11.0589439Z         
2025-03-14T05:08:11.0589823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0590285Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0590543Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:11.0590779Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:11.0591055Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0591319Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:11.0591567Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:11.0591793Z         
2025-03-14T05:08:11.0592169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:11.0593150Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0593853Z         
2025-03-14T05:08:11.0594307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:11.0594880Z         deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:08:11.0595170Z         
2025-03-14T05:08:11.0595590Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:11.0596149Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:11.0596431Z         
2025-03-14T05:08:11.0596832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:11.0597326Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:11.0597626Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0597943Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:11.0598204Z         
2025-03-14T05:08:11.0598605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:11.0599118Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:11.0599410Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:11.0599732Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:11.0600016Z         
2025-03-14T05:08:11.0600420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:11.0600905Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0601165Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:11.0601419Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:11.0601663Z         
2025-03-14T05:08:11.0602063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:11.0602575Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:11.0602866Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:11.0603133Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:11.0603376Z         
2025-03-14T05:08:11.0603788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:11.0604301Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:11.0604629Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:11.0604873Z         
2025-03-14T05:08:11.0605248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:11.0605742Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:11.0606089Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:11.0606317Z         
2025-03-14T05:08:11.0606693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:11.0607177Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:11.0607490Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:11.0607717Z         
2025-03-14T05:08:11.0608102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:11.0608647Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:11.0608985Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:11.0609215Z         
2025-03-14T05:08:11.0609630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:11.0610147Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:11.0610399Z         
2025-03-14T05:08:11.0610807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:11.0611312Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:11.0611575Z         
2025-03-14T05:08:11.0611994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:11.0612514Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:11.0612842Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:11.0613165Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:11.0613501Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:11.0613755Z         
2025-03-14T05:08:11.0614180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:11.0614701Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:11.0615011Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:11.0615326Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:11.0615661Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:11.0615914Z         
2025-03-14T05:08:11.0616323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:11.0616814Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:11.0617134Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:11.0617468Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:11.0617715Z         
2025-03-14T05:08:11.0618120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:11.0618603Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:11.0618953Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:11.0619299Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:11.0619544Z         
2025-03-14T05:08:11.0619943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:11.0620405Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:11.0620673Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:11.0620918Z         
2025-03-14T05:08:11.0621321Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:11.0621788Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:11.0622057Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:11.0622301Z         
2025-03-14T05:08:11.0622696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:11.0623376Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:11.0623764Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:11.0624017Z         
2025-03-14T05:08:11.0624488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:11.0625029Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:11.0625350Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:11.0625640Z         
2025-03-14T05:08:11.0626253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:11.0626900Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:11.0627206Z         
2025-03-14T05:08:11.0627666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:11.0628295Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:11.0628600Z         
2025-03-14T05:08:11.0629313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:11.0629947Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:11.0630325Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:11.0630628Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:11.0630941Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:11.0631271Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:11.0631544Z         
2025-03-14T05:08:11.0632024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0632672Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0633025Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:11.0633446Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:11.0633832Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0634208Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:11.0634465Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:11.0634689Z         
2025-03-14T05:08:11.0635112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:11.0635700Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:08:11.0635989Z         
2025-03-14T05:08:11.0636448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:11.0637071Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:11.0637447Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:11.0637738Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:11.0638040Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:11.0638356Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:11.0638620Z         
2025-03-14T05:08:11.0639174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:11.0639891Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:11.0640236Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:11.0640591Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:11.0640933Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:11.0641227Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:11.0641469Z         
2025-03-14T05:08:11.0641912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:11.0642430Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:11.0642666Z         
2025-03-14T05:08:11.0642761Z 
2025-03-14T05:08:11.0642851Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:11.0644217Z     def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:11.0645545Z         l_stack0_ = L_stack0_
2025-03-14T05:08:11.0645935Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:08:11.0646509Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:08:11.0647117Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:08:11.0647678Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:08:11.0648153Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0648557Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0648951Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0649349Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0649644Z         
2025-03-14T05:08:11.0650179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:08:11.0650816Z         mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:08:11.0651086Z         
2025-03-14T05:08:11.0651489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:08:11.0652447Z         scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:08:11.0653167Z         
2025-03-14T05:08:11.0653561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:08:11.0654564Z         proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:08:11.0655298Z         
2025-03-14T05:08:11.0655674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0656136Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0656392Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:11.0656626Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:11.0656896Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0657154Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:11.0657397Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:11.0657613Z         
2025-03-14T05:08:11.0657977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:11.0658898Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0659607Z         
2025-03-14T05:08:11.0660072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:11.0660679Z         deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:08:11.0660961Z         
2025-03-14T05:08:11.0661358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:11.0661885Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:11.0662167Z         
2025-03-14T05:08:11.0662572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:11.0663077Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:11.0663386Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0663705Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:11.0664181Z         
2025-03-14T05:08:11.0664634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:11.0665159Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:11.0665473Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:11.0665806Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:11.0666085Z         
2025-03-14T05:08:11.0666510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:11.0667018Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0667276Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:11.0667540Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:11.0667797Z         
2025-03-14T05:08:11.0668196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:11.0668710Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:11.0668996Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:11.0669259Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:11.0669502Z         
2025-03-14T05:08:11.0669910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:11.0670424Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:11.0670749Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:11.0670985Z         
2025-03-14T05:08:11.0671373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:11.0671876Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:11.0672194Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:11.0672427Z         
2025-03-14T05:08:11.0672812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:11.0673313Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:11.0673629Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:11.0673892Z         
2025-03-14T05:08:11.0674314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:11.0674849Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:11.0675195Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:11.0675421Z         
2025-03-14T05:08:11.0675846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:11.0676369Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:11.0676627Z         
2025-03-14T05:08:11.0677081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:11.0677604Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:11.0677861Z         
2025-03-14T05:08:11.0678363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:11.0679106Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:11.0679513Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:11.0679883Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:11.0680277Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:11.0680539Z         
2025-03-14T05:08:11.0680982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:11.0681693Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:11.0682178Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:11.0682642Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:11.0682985Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:11.0683245Z         
2025-03-14T05:08:11.0683667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:11.0684179Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:11.0684508Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:11.0684853Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:11.0685112Z         
2025-03-14T05:08:11.0685543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:11.0686047Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:11.0686379Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:11.0686728Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:11.0686985Z         
2025-03-14T05:08:11.0687386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:11.0687863Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:11.0688212Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:11.0688449Z         
2025-03-14T05:08:11.0688832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:11.0689276Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:11.0689533Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:11.0689763Z         
2025-03-14T05:08:11.0690147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:11.0690609Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:11.0690894Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:11.0691135Z         
2025-03-14T05:08:11.0691517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:11.0691975Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:11.0692254Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:11.0692492Z         
2025-03-14T05:08:11.0692908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:11.0693463Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:11.0693778Z         
2025-03-14T05:08:11.0694194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:11.0694738Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:11.0695045Z         
2025-03-14T05:08:11.0695479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:11.0696078Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:11.0696430Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:11.0696713Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:11.0697013Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:11.0697322Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:11.0697569Z         
2025-03-14T05:08:11.0697965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0698506Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0698843Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:11.0699085Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:11.0699437Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0699781Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:11.0700026Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:11.0700243Z         
2025-03-14T05:08:11.0700648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:11.0701223Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:08:11.0701509Z         
2025-03-14T05:08:11.0701939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:11.0702520Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:11.0702871Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:11.0703154Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:11.0703451Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:11.0703757Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:11.0704011Z         
2025-03-14T05:08:11.0704646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:11.0705373Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:11.0705733Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:11.0706091Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:11.0706443Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:11.0706746Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:11.0706988Z         
2025-03-14T05:08:11.0707422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:11.0707944Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:11.0708175Z         
2025-03-14T05:08:11.0708308Z 
2025-03-14T05:08:11.0708399Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:11.0709721Z     def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:11.0711004Z         l_stack0_ = L_stack0_
2025-03-14T05:08:11.0711400Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:08:11.0711966Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:08:11.0712524Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:08:11.0713089Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:08:11.0713567Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0714004Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0714433Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0714831Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0715124Z         
2025-03-14T05:08:11.0715649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:08:11.0716274Z         mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:08:11.0716541Z         
2025-03-14T05:08:11.0716932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:08:11.0717886Z         scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:08:11.0718602Z         
2025-03-14T05:08:11.0719012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:08:11.0720007Z         proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:08:11.0720771Z         
2025-03-14T05:08:11.0721141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0721611Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0721868Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:11.0722103Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:11.0722377Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0722638Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:11.0722884Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:11.0723102Z         
2025-03-14T05:08:11.0723466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:11.0724376Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0725069Z         
2025-03-14T05:08:11.0725521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:11.0726080Z         deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:08:11.0726351Z         
2025-03-14T05:08:11.0726743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:11.0727260Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:11.0727535Z         
2025-03-14T05:08:11.0727967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:11.0728495Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:11.0728899Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0729346Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:11.0729604Z         
2025-03-14T05:08:11.0730001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:11.0730562Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:11.0731040Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:11.0731381Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:11.0731640Z         
2025-03-14T05:08:11.0732028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:11.0732501Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0732754Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:11.0733003Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:11.0733237Z         
2025-03-14T05:08:11.0733623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:11.0734294Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:11.0734598Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:11.0734853Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:11.0735088Z         
2025-03-14T05:08:11.0735505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:11.0736004Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:11.0736324Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:11.0736553Z         
2025-03-14T05:08:11.0736936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:11.0737431Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:11.0737749Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:11.0737976Z         
2025-03-14T05:08:11.0738359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:11.0738849Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:11.0739160Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:11.0739393Z         
2025-03-14T05:08:11.0739780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:11.0740312Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:11.0740658Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:11.0740886Z         
2025-03-14T05:08:11.0741346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:11.0741878Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:11.0742135Z         
2025-03-14T05:08:11.0742557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:11.0743082Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:11.0743348Z         
2025-03-14T05:08:11.0743809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:11.0744449Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:11.0744811Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:11.0745185Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:11.0745587Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:11.0745848Z         
2025-03-14T05:08:11.0746285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:11.0746829Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:11.0747164Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:11.0747527Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:11.0747885Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:11.0748156Z         
2025-03-14T05:08:11.0748600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:11.0749153Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:11.0749494Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:11.0749850Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:11.0750115Z         
2025-03-14T05:08:11.0750535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:11.0751038Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:11.0751366Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:11.0751713Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:11.0751967Z         
2025-03-14T05:08:11.0752366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:11.0752826Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:11.0753090Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:11.0753327Z         
2025-03-14T05:08:11.0753751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:11.0754205Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:11.0754465Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:11.0754698Z         
2025-03-14T05:08:11.0755117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:11.0755600Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:11.0755894Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:11.0756143Z         
2025-03-14T05:08:11.0756532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:11.0757000Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:11.0757288Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:11.0757533Z         
2025-03-14T05:08:11.0757971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:11.0758554Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:11.0758842Z         
2025-03-14T05:08:11.0759263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:11.0759813Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:11.0760099Z         
2025-03-14T05:08:11.0760540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:11.0761170Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:11.0761530Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:11.0761833Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:11.0762129Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:11.0762432Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:11.0762690Z         
2025-03-14T05:08:11.0763060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0763604Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0763948Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:11.0764188Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:11.0764546Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0764894Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:11.0765138Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:11.0765353Z         
2025-03-14T05:08:11.0765775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:11.0766313Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:08:11.0766594Z         
2025-03-14T05:08:11.0767020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:11.0767602Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:11.0767986Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:11.0768271Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:11.0768564Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:11.0768872Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:11.0769123Z         
2025-03-14T05:08:11.0769656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:11.0770327Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:11.0770659Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:11.0770993Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:11.0771328Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:11.0771613Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:11.0771848Z         
2025-03-14T05:08:11.0772276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:11.0772781Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:11.0773010Z         
2025-03-14T05:08:11.0773146Z 
2025-03-14T05:08:11.0773236Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:11.0774570Z     def forward(self, L_stack0_: "f32[3272, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:11.0775861Z         l_stack0_ = L_stack0_
2025-03-14T05:08:11.0776239Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:08:11.0776801Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:08:11.0777362Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:08:11.0777935Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:08:11.0778436Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0778839Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0779237Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0779631Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:11.0779926Z         
2025-03-14T05:08:11.0780455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:08:11.0781127Z         mean: "f32[3272, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:08:11.0781402Z         
2025-03-14T05:08:11.0781972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:08:11.0782976Z         scores: "f32[3272, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:08:11.0783719Z         
2025-03-14T05:08:11.0784184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:08:11.0785235Z         proposal_deltas: "f32[3272, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:08:11.0786001Z         
2025-03-14T05:08:11.0786383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0786863Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0787135Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:11.0787371Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:11.0787701Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:11.0787964Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:11.0788209Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:11.0788433Z         
2025-03-14T05:08:11.0788829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:11.0789771Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0790481Z         
2025-03-14T05:08:11.0790942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:11.0791515Z         deltas: "f32[3272, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:08:11.0791789Z         
2025-03-14T05:08:11.0792186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:11.0792709Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:11.0792979Z         
2025-03-14T05:08:11.0793380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:11.0793875Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:11.0794177Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0794494Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:11.0794753Z         
2025-03-14T05:08:11.0795214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:11.0795707Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:11.0795997Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:11.0796308Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:11.0796569Z         
2025-03-14T05:08:11.0796968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:11.0797451Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:11.0797711Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:11.0797969Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:11.0798206Z         
2025-03-14T05:08:11.0798606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:11.0799118Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:11.0799400Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:11.0799658Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:11.0799896Z         
2025-03-14T05:08:11.0800283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:11.0800778Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:11.0801115Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:11.0801345Z         
2025-03-14T05:08:11.0801728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:11.0802232Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:11.0802543Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:11.0802769Z         
2025-03-14T05:08:11.0803142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:11.0803626Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:11.0803939Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:11.0804166Z         
2025-03-14T05:08:11.0804542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:11.0805057Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:11.0805395Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:11.0805625Z         
2025-03-14T05:08:11.0806032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:11.0806543Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:11.0806796Z         
2025-03-14T05:08:11.0807198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:11.0807699Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:11.0807944Z         
2025-03-14T05:08:11.0808392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:11.0808912Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:11.0809218Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:11.0809536Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:11.0809864Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:11.0810117Z         
2025-03-14T05:08:11.0810539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:11.0811058Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:11.0811364Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:11.0811679Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:11.0812012Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:11.0812261Z         
2025-03-14T05:08:11.0812670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:11.0813154Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:11.0813492Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:11.0813825Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:11.0814072Z         
2025-03-14T05:08:11.0814481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:11.0814987Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:11.0815308Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:11.0815652Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:11.0815899Z         
2025-03-14T05:08:11.0816289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:11.0816740Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:11.0816997Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:11.0817229Z         
2025-03-14T05:08:11.0817615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:11.0818059Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:11.0818315Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:11.0818544Z         
2025-03-14T05:08:11.0818924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:11.0819384Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:11.0819670Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:11.0819909Z         
2025-03-14T05:08:11.0820287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:11.0820783Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:11.0821064Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:11.0821308Z         
2025-03-14T05:08:11.0821728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:11.0822292Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:11.0822577Z         
2025-03-14T05:08:11.0822985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:11.0823529Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:11.0823812Z         
2025-03-14T05:08:11.0824346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:11.0824990Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:11.0825373Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:11.0825659Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:11.0825977Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:11.0826329Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:11.0826600Z         
2025-03-14T05:08:11.0826986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:11.0827557Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0827961Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:11.0828219Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:11.0828600Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:11.0828969Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:11.0829230Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:11.0829465Z         
2025-03-14T05:08:11.0829908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:11.0830495Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:08:11.0830801Z         
2025-03-14T05:08:11.0831267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:11.0831893Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:11.0832267Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:11.0832572Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:11.0832888Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:11.0833220Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:11.0833490Z         
2025-03-14T05:08:11.0834103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:11.0834798Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:11.0835161Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:11.0835512Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:11.0835864Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:11.0836168Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:11.0836414Z         
2025-03-14T05:08:11.0836860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:11.0837389Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:11.0837636Z         
2025-03-14T05:08:12.7210207Z 
2025-03-14T05:08:12.7211066Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:12.7212130Z     def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:12.7213063Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:08:12.7213306Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:08:12.7213980Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7214396Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7214812Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7215272Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7215570Z         
2025-03-14T05:08:12.7216002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7216495Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7216761Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:12.7217006Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:12.7217289Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7217566Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:12.7217821Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:12.7218052Z         
2025-03-14T05:08:12.7218451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:12.7219425Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7220166Z         
2025-03-14T05:08:12.7220649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:12.7221250Z         deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:08:12.7221533Z         
2025-03-14T05:08:12.7222033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:12.7222589Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:12.7222877Z         
2025-03-14T05:08:12.7223297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:12.7223816Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:12.7224245Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7224590Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:12.7224868Z         
2025-03-14T05:08:12.7225296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:12.7225823Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:12.7226137Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:12.7226469Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:12.7226741Z         
2025-03-14T05:08:12.7227156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:12.7227661Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7227930Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:12.7228226Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:12.7228472Z         
2025-03-14T05:08:12.7228886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:12.7229426Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:12.7229719Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:12.7229993Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:12.7230245Z         
2025-03-14T05:08:12.7230668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:12.7231201Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:12.7231544Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:12.7231780Z         
2025-03-14T05:08:12.7232193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:12.7232705Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:12.7233031Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:12.7233264Z         
2025-03-14T05:08:12.7233659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:12.7234159Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:12.7234480Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:12.7234715Z         
2025-03-14T05:08:12.7235112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:12.7235733Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:12.7236094Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:12.7236334Z         
2025-03-14T05:08:12.7236780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:12.7237341Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:12.7237599Z         
2025-03-14T05:08:12.7238026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:12.7238554Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:12.7238808Z         
2025-03-14T05:08:12.7239242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:12.7239787Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:12.7240110Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:12.7240439Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:12.7240788Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:12.7241048Z         
2025-03-14T05:08:12.7241483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:12.7242042Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:12.7242360Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:12.7242699Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:12.7243038Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:12.7243290Z         
2025-03-14T05:08:12.7243707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:12.7244210Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:12.7244534Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:12.7244874Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:12.7245124Z         
2025-03-14T05:08:12.7245546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:12.7246051Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:12.7246379Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:12.7246726Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:12.7246976Z         
2025-03-14T05:08:12.7247374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:12.7247841Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:12.7248110Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:12.7248345Z         
2025-03-14T05:08:12.7248780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:12.7249245Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:12.7249506Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:12.7249733Z         
2025-03-14T05:08:12.7250125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:12.7250602Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:12.7250892Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:12.7251145Z         
2025-03-14T05:08:12.7251539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:12.7252013Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:12.7252303Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:12.7252552Z         
2025-03-14T05:08:12.7252985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:12.7253570Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:12.7253864Z         
2025-03-14T05:08:12.7254280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:12.7254851Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:12.7255141Z         
2025-03-14T05:08:12.7255606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:12.7256238Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:12.7256603Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:12.7256899Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:12.7257205Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:12.7257526Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:12.7257789Z         
2025-03-14T05:08:12.7258170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7258729Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7259079Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:12.7259323Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:12.7259681Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7260032Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:12.7260281Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:12.7260502Z         
2025-03-14T05:08:12.7260922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:12.7261516Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:08:12.7261838Z         
2025-03-14T05:08:12.7262341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:12.7262944Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:12.7263513Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:12.7263818Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:12.7264187Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:12.7264540Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:12.7264821Z         
2025-03-14T05:08:12.7265411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:12.7266136Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:12.7266504Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:12.7266901Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:12.7267264Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:12.7267578Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:12.7267833Z         
2025-03-14T05:08:12.7268300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:12.7268880Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:12.7269135Z         
2025-03-14T05:08:12.7279152Z 
2025-03-14T05:08:12.7279669Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:12.7280692Z     def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:12.7281788Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:08:12.7282045Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:08:12.7282386Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7282808Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7283226Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7283639Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7283958Z         
2025-03-14T05:08:12.7284372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7284845Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7285100Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:12.7285337Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:12.7285614Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7285878Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:12.7286126Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:12.7286347Z         
2025-03-14T05:08:12.7286861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:12.7287856Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7288582Z         
2025-03-14T05:08:12.7289061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:12.7289649Z         deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:08:12.7289929Z         
2025-03-14T05:08:12.7290335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:12.7290864Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:12.7291146Z         
2025-03-14T05:08:12.7291551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:12.7292057Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:12.7292364Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7292774Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:12.7293179Z         
2025-03-14T05:08:12.7293812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:12.7294448Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:12.7294776Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:12.7295101Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:12.7295371Z         
2025-03-14T05:08:12.7295787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:12.7296310Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7296590Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:12.7296864Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:12.7297119Z         
2025-03-14T05:08:12.7297537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:12.7298068Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:12.7298368Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:12.7298644Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:12.7298901Z         
2025-03-14T05:08:12.7299330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:12.7299848Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:12.7300178Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:12.7300421Z         
2025-03-14T05:08:12.7300820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:12.7301368Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:12.7301692Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:12.7301926Z         
2025-03-14T05:08:12.7302310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:12.7302808Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:12.7303130Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:12.7303364Z         
2025-03-14T05:08:12.7303755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:12.7304416Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:12.7304788Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:12.7305039Z         
2025-03-14T05:08:12.7305490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:12.7306023Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:12.7306284Z         
2025-03-14T05:08:12.7306702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:12.7307251Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:12.7307507Z         
2025-03-14T05:08:12.7307942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:12.7308491Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:12.7308810Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:12.7309141Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:12.7309485Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:12.7309743Z         
2025-03-14T05:08:12.7310173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:12.7310711Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:12.7311025Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:12.7311353Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:12.7311695Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:12.7311943Z         
2025-03-14T05:08:12.7312359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:12.7312860Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:12.7313182Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:12.7313523Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:12.7313775Z         
2025-03-14T05:08:12.7314225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:12.7314747Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:12.7315076Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:12.7315424Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:12.7315676Z         
2025-03-14T05:08:12.7316072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:12.7316536Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:12.7316800Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:12.7317035Z         
2025-03-14T05:08:12.7317430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:12.7317886Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:12.7318144Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:12.7318374Z         
2025-03-14T05:08:12.7318763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:12.7319236Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:12.7319523Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:12.7319802Z         
2025-03-14T05:08:12.7320188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:12.7320649Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:12.7320935Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:12.7321209Z         
2025-03-14T05:08:12.7321629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:12.7322194Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:12.7322482Z         
2025-03-14T05:08:12.7322889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:12.7323428Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:12.7323747Z         
2025-03-14T05:08:12.7324181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:12.7324773Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:12.7325125Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:12.7325406Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:12.7325703Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:12.7326014Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:12.7326268Z         
2025-03-14T05:08:12.7326637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7327204Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7327546Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:12.7327785Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:12.7328142Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7328488Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:12.7328736Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:12.7328957Z         
2025-03-14T05:08:12.7329384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:12.7329987Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:08:12.7330309Z         
2025-03-14T05:08:12.7330747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:12.7331341Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:12.7331694Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:12.7331978Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:12.7332270Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:12.7332574Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:12.7332846Z         
2025-03-14T05:08:12.7333380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:12.7334053Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:12.7334411Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:12.7334740Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:12.7335072Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:12.7335360Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:12.7335598Z         
2025-03-14T05:08:12.7336019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:12.7336533Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:12.7336754Z         
2025-03-14T05:08:12.7336846Z 
2025-03-14T05:08:12.7336938Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:12.7337732Z     def forward(self, L_predictions_0_: "f32[3272, 81][81, 1]cpu", L_predictions_1_: "f32[3272, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1272 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1272 - s0, 4][4, 1]cpu"):
2025-03-14T05:08:12.7338526Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:08:12.7338756Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:08:12.7339062Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7339466Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7339865Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7340288Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:08:12.7340580Z         
2025-03-14T05:08:12.7340963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7341423Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7341679Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:08:12.7341916Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:08:12.7342220Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:08:12.7342481Z         getitem_2: "Sym(1272 - s0)" = size_1[0]
2025-03-14T05:08:12.7342726Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:08:12.7342949Z         
2025-03-14T05:08:12.7343318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:08:12.7344358Z         proposal_boxes: "f32[3272, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7345097Z         
2025-03-14T05:08:12.7345573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:08:12.7346150Z         deltas: "f32[3272, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:08:12.7346463Z         
2025-03-14T05:08:12.7346861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:08:12.7347382Z         boxes: "f32[3272, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:08:12.7347691Z         
2025-03-14T05:08:12.7348095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:08:12.7348588Z         getitem_4: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:08:12.7348890Z         getitem_5: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7349210Z         widths: "f32[3272][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:08:12.7349472Z         
2025-03-14T05:08:12.7349876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:08:12.7350364Z         getitem_6: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:08:12.7350658Z         getitem_7: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:08:12.7350978Z         heights: "f32[3272][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:08:12.7351237Z         
2025-03-14T05:08:12.7351632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:08:12.7352113Z         getitem_8: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:08:12.7352371Z         mul: "f32[3272][1]cpu" = 0.5 * widths
2025-03-14T05:08:12.7352623Z         ctr_x: "f32[3272][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:08:12.7352861Z         
2025-03-14T05:08:12.7353260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:08:12.7355003Z         getitem_9: "f32[3272][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:08:12.7355294Z         mul_1: "f32[3272][1]cpu" = 0.5 * heights
2025-03-14T05:08:12.7355561Z         ctr_y: "f32[3272][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:08:12.7355797Z         
2025-03-14T05:08:12.7356202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:08:12.7356718Z         getitem_10: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:08:12.7357046Z         dx: "f32[3272, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:08:12.7357286Z         
2025-03-14T05:08:12.7357685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:08:12.7358178Z         getitem_11: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:08:12.7358499Z         dy: "f32[3272, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:08:12.7358738Z         
2025-03-14T05:08:12.7359123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:08:12.7359625Z         getitem_12: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:08:12.7359948Z         dw: "f32[3272, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:08:12.7360190Z         
2025-03-14T05:08:12.7360568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:08:12.7361107Z         getitem_13: "f32[3272, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:08:12.7361442Z         dh: "f32[3272, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:08:12.7361686Z         
2025-03-14T05:08:12.7362098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:08:12.7362611Z         dw_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:08:12.7362864Z         
2025-03-14T05:08:12.7363269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:08:12.7363780Z         dh_1: "f32[3272, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:08:12.7364034Z         
2025-03-14T05:08:12.7364463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:08:12.7365010Z         getitem_14: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:08:12.7365319Z         mul_2: "f32[3272, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:08:12.7365645Z         getitem_15: "f32[3272, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:08:12.7365987Z         pred_ctr_x: "f32[3272, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:08:12.7366244Z         
2025-03-14T05:08:12.7366674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:08:12.7367227Z         getitem_16: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:08:12.7367535Z         mul_3: "f32[3272, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:08:12.7367885Z         getitem_17: "f32[3272, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:08:12.7368227Z         pred_ctr_y: "f32[3272, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:08:12.7368483Z         
2025-03-14T05:08:12.7368896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:08:12.7369392Z         exp: "f32[3272, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:08:12.7369716Z         getitem_18: "f32[3272, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:08:12.7370055Z         pred_w: "f32[3272, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:08:12.7370303Z         
2025-03-14T05:08:12.7370717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:08:12.7371216Z         exp_1: "f32[3272, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:08:12.7371542Z         getitem_19: "f32[3272, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:08:12.7371887Z         pred_h: "f32[3272, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:08:12.7372140Z         
2025-03-14T05:08:12.7372540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:08:12.7372998Z         mul_6: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:08:12.7373299Z         x1: "f32[3272, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:08:12.7373538Z         
2025-03-14T05:08:12.7373922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:08:12.7374377Z         mul_7: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:08:12.7374654Z         y1: "f32[3272, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:08:12.7374894Z         
2025-03-14T05:08:12.7375292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:08:12.7375768Z         mul_8: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:08:12.7376068Z         x2: "f32[3272, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:08:12.7376322Z         
2025-03-14T05:08:12.7376717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:08:12.7377190Z         mul_9: "f32[3272, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:08:12.7377485Z         y2: "f32[3272, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:08:12.7377738Z         
2025-03-14T05:08:12.7378177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:08:12.7378753Z         pred_boxes: "f32[3272, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:08:12.7379053Z         
2025-03-14T05:08:12.7379470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:08:12.7380028Z         predict_boxes: "f32[3272, 320][320, 1]cpu" = pred_boxes.reshape((3272, 320));  pred_boxes = None
2025-03-14T05:08:12.7380316Z         
2025-03-14T05:08:12.7380795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:08:12.7381402Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:08:12.7381960Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:08:12.7382253Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:08:12.7382561Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:08:12.7382966Z         getitem_23: "f32[1272 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:08:12.7395460Z         
2025-03-14T05:08:12.7395945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:08:12.7396558Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7396929Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:08:12.7397192Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:08:12.7397576Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:08:12.7397950Z         getitem_26: "Sym(1272 - s0)" = size_3[0]
2025-03-14T05:08:12.7398209Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:08:12.7398439Z         
2025-03-14T05:08:12.7398884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:08:12.7399649Z         probs: "f32[3272, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:08:12.7399992Z         
2025-03-14T05:08:12.7400468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:08:12.7401133Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:08:12.7401511Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:08:12.7401820Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:08:12.7402136Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:08:12.7402469Z         getitem_31: "f32[1272 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:08:12.7402744Z         
2025-03-14T05:08:12.7403322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:12.7404037Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:08:12.7404399Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:12.7404749Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:08:12.7405103Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:12.7405400Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:12.7405652Z         
2025-03-14T05:08:12.7406115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:12.7406655Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:12.7406895Z         
2025-03-14T05:08:15.0354166Z 
2025-03-14T05:08:15.0355009Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:15.0355862Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:08:15.0356193Z         l_scores_0_ = L_scores_0_
2025-03-14T05:08:15.0356410Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:08:15.0356609Z         
2025-03-14T05:08:15.0357218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:15.0357943Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:08:15.0358285Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:15.0358623Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:08:15.0358956Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:15.0359261Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:15.0359516Z         
2025-03-14T05:08:15.0359977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:15.0360512Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:15.0360763Z         
2025-03-14T05:08:15.0360854Z 
2025-03-14T05:08:15.0360956Z class GraphModule(torch.nn.Module):
2025-03-14T05:08:15.0361266Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:08:15.0361623Z         l_scores_0_ = L_scores_0_
2025-03-14T05:08:15.0361840Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:08:15.0362040Z         
2025-03-14T05:08:15.0362602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:08:15.0363302Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:08:15.0363631Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:08:15.0363949Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:08:15.0364285Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:08:15.0364592Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:08:15.0364830Z         
2025-03-14T05:08:15.0365263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:08:15.0365770Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:08:15.0366003Z         
2025-03-14T05:08:58.3807018Z E0314 05:08:58.379602 61109 site-packages/torch/_dynamo/utils.py:2789] Accuracy failed: uint8 tensor did not match
2025-03-14T05:08:58.3810515Z E0314 05:08:58.380072 61109 site-packages/torch/_dynamo/utils.py:2752] Accuracy failed for key name pred_masks
2025-03-14T05:08:58.3813527Z E0314 05:08:58.380577 61109 site-packages/torch/_dynamo/utils.py:2752] Accuracy failed for key name instances
2025-03-14T05:08:58.4740761Z Compilation time (from dynamo_timed): 62.065702157
2025-03-14T05:08:58.4746527Z fail_accuracy
2025-03-14T05:08:58.4747130Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:08:58.4748236Z TIMING: entire_frame_compile:62.0657 gc:0.04684 _recursive_pre_grad_passes:0.03885 async_compile.wait:22.26888 backend_compile:43.08486 _recursive_joint_graph_passes:0.23827 inductor_compile:28.41757 _recursive_post_grad_passes:0.09874 code_gen:25.59921 total_wall_time:62.0657
2025-03-14T05:08:58.4749950Z STATS: call_* op count: 936 | FakeTensorMode.__torch_dispatch__:22007 | FakeTensor.__torch_dispatch__:1491 | ProxyTorchDispatchMode.__torch_dispatch__:4733 | attempt fast:415 | slow no contiguity match:46 | fast is_contiguous:359 | slow both tensors nontrivially broadcast:10
2025-03-14T05:08:58.4750756Z Dynamo produced 67 graphs covering 936 ops with 56 graph breaks (8 unique)
2025-03-14T05:09:04.6367960Z 
2025-03-14T05:09:14.5160548Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:09:14.5162827Z loading model: 0it [00:09, ?it/s]
2025-03-14T05:09:14.5175460Z cpu  eval  detectron2_maskrcnn_r_101_fpn      
2025-03-14T05:09:30.0859519Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_101_fpn. Setting accuracy check to cosine
2025-03-14T05:09:30.1029875Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:09:38.9366359Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:09:48.0355825Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:00.4705010Z 
2025-03-14T05:10:00.4705570Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:00.4854609Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:10:00.4974056Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:10:00.4974575Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:10:00.4975358Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.4976175Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.4977121Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.4977891Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.4978682Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.4979520Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.4980563Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.4981647Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.4982613Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.4983543Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.4984610Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.4985770Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.4986741Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.4987685Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.4988590Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.4989523Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.4990525Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.4991482Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.4992360Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.4993282Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.4994250Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.4995224Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.4996102Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.4996960Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.4997761Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.4999273Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5000153Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5001012Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5001899Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5002703Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5003542Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5004428Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5005725Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5006565Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5007364Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5008162Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5009400Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5010260Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5011061Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5011832Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5012631Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5013475Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5014304Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5015105Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5015916Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5016711Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5017552Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5018375Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5019179Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5019945Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5020742Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5021579Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5022442Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5023812Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5024684Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5025507Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5026372Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5027224Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5028049Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5028829Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5029634Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5030537Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5031389Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5032533Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5033307Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5034115Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5034962Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5035788Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5037248Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5038065Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.5038916Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5039792Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5040643Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.5041468Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.5042238Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5043027Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5043867Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5044716Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5045507Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5046266Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5047049Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5047887Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5048705Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5049557Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5050329Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5051155Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5052009Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5052853Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5053649Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5054413Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5055200Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5056046Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5056880Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5057672Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5058461Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5060182Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5061045Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5061874Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5062674Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5063453Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5064332Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5065252Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5066137Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5067000Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5067808Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5068696Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5069593Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5070461Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5071295Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5072094Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5072926Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5073852Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5074714Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5075550Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5076334Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5077119Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5077956Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5078767Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5079569Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5080323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5081127Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5082100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5082946Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5083765Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5084526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5085325Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5086167Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5087091Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5087884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5088664Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5092612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5093549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5094411Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5095235Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5096037Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.5096980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5097936Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5098813Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.5099659Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.5100457Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5101279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5102153Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5102997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5103818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5104713Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5105536Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5108575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5109460Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5110279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5111060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5111865Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5112739Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5113629Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5114441Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5115205Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5116000Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5116847Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5117665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5118459Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5119220Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5120050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5120901Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5121726Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5122530Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5123298Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5124551Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5125409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5126236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5127064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5127831Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5128642Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5129482Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5130297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5131087Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5131848Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5132635Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5133477Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5134327Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5135122Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5135881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5136675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5137530Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5138355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5139145Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5139910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5140720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5141588Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5142405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5143198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5143961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5144839Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5145700Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5146543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5148170Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5148957Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5149779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5150651Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5151498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5152319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5153102Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5153910Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5155225Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5156086Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5156930Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5157712Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5158529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5159394Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5160232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5161042Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5161829Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5162657Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5163498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5164321Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5165114Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5165876Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5166673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5167517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5168336Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5169604Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5170403Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5171195Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5172040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5172863Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5173663Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5174873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5175673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5176519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5177380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5178179Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5178938Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5179727Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5180571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5181393Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5182423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5183210Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5184072Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5185036Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5185886Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5186704Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5187489Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5188301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5189165Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5190002Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5190882Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5191676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5192481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5193340Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5194180Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5194995Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5195773Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5196585Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5197472Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5198301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5199117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5199881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5200675Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5201521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5202342Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5203136Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5203894Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5204722Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5205575Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5206400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5207201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5208756Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5209582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5210445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5211269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5212097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5212881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5213668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5214501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5215322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5216116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5216873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5217672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5218521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5219379Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5220184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5220949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5221750Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5222596Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5223421Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5224296Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5225131Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5225966Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5226858Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5227721Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5228548Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5229334Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5230989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5231868Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5232720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5233603Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5234398Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5235223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5236113Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5236961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5237786Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5238567Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5239379Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5240273Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5241130Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5241925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5242686Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5243487Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5244329Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5245165Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5245965Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5246729Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5247594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5248445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5249535Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5250345Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5251108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5251909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5252757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5253607Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5254405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5255194Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5256358Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5257240Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5258079Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5258894Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5259666Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5260472Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5261378Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5262227Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5263026Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5263783Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5264699Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5265589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5266437Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5267255Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5268075Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5268922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5269804Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5270661Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5271499Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5272293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5273120Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5273993Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5276397Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5277245Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5278034Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5278853Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5279725Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5280559Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5281371Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5282311Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5283172Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5284021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5284871Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5285667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5286434Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5287236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5288086Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5288907Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5289701Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5290513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5291306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5292158Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5292990Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5293790Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5294554Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5295341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5296182Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5297016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5297836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5298595Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5299382Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5301299Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5302164Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5302996Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5303797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5304738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5305674Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5306527Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5307345Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5308125Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5309388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5310269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5311116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5311975Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5312757Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5313589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5314453Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5315301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5316124Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5316914Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5317715Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5318565Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5319414Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5320213Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5320985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5321783Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5322630Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5323449Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5324243Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5325003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5325817Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5326680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5327510Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5328312Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5329073Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5329861Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5330705Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5331518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5332344Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5333108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5333900Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5334741Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5335568Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5336360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5337115Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5337905Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5339433Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5340278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5341108Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5341890Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5342708Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5343574Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5344493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5345324Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5346116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5347045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5347919Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5348768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5349574Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5350362Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5351176Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5352045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5352883Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5353721Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5354516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5355329Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5356191Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5357037Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5357833Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5358592Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5359376Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5360251Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5361077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5361873Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5362645Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5363437Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5364280Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5365754Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5366585Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5367393Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5368213Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5369096Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5369934Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5370726Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5371489Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5372282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5373128Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5373985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5374839Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5375680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5376830Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5377983Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5379184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5380384Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5381706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5382997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5384410Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5385427Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5386498Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5387294Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5388203Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5389179Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5390131Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5390943Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5391798Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5392636Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5393587Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5394429Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5395343Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5396372Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5397430Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5398292Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5399148Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5400056Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5400873Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.5401719Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5402609Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5403483Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.5404329Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.5405120Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5405931Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5406830Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5407673Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5408533Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5409331Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5411914Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5412820Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5413669Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5414488Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5415505Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5416353Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5417215Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5418439Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5419325Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5420113Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.5420971Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5422209Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5423289Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.5424431Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.5425659Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.5426522Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5427694Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5428972Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.5430023Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.5431106Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.5432044Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.5433297Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.5434449Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.5435446Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.5436276Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:10:00.5436882Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:10:00.5437508Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:10:00.5438120Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:10:00.5438633Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:10:00.5439309Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:10:00.5439983Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:10:00.5442171Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:10:00.5442872Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:10:00.5443517Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:10:00.5444045Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:10:00.5444536Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:10:00.5445032Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:10:00.5445527Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:10:00.5446029Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:10:00.5446529Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:10:00.5447158Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:10:00.5447921Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:10:00.5448679Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:10:00.5449876Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:10:00.5450638Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:10:00.5451387Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:10:00.5452077Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:10:00.5452822Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:10:00.5453606Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:10:00.5454374Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:10:00.5455123Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:10:00.5455598Z         
2025-03-14T05:10:00.5456003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5456884Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5457573Z         
2025-03-14T05:10:00.5457952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5460843Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5463129Z         
2025-03-14T05:10:00.5463577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:10:00.5464164Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:10:00.5464542Z         
2025-03-14T05:10:00.5465057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:10:00.5465814Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:10:00.5466194Z         
2025-03-14T05:10:00.5466569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5467447Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5468083Z         
2025-03-14T05:10:00.5468462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5470698Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5472726Z         
2025-03-14T05:10:00.5473130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5473650Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:10:00.5473962Z         
2025-03-14T05:10:00.5474310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5475123Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5475733Z         
2025-03-14T05:10:00.5476088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5478218Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5480108Z         
2025-03-14T05:10:00.5480483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5480966Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:10:00.5481259Z         
2025-03-14T05:10:00.5481749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5482573Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5483191Z         
2025-03-14T05:10:00.5483552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5485656Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5490482Z         
2025-03-14T05:10:00.5491082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5491938Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.5492571Z         
2025-03-14T05:10:00.5492939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5498680Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5500840Z         
2025-03-14T05:10:00.5501309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5501815Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:10:00.5502090Z         
2025-03-14T05:10:00.5502499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5503749Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:10:00.5504038Z         
2025-03-14T05:10:00.5504455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5505275Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5505932Z         
2025-03-14T05:10:00.5506291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5510204Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5520093Z         
2025-03-14T05:10:00.5520699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5521398Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:10:00.5521670Z         
2025-03-14T05:10:00.5522187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5523162Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5523792Z         
2025-03-14T05:10:00.5524149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5526220Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5528173Z         
2025-03-14T05:10:00.5528555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5529048Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:10:00.5529318Z         
2025-03-14T05:10:00.5529663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5530497Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5531117Z         
2025-03-14T05:10:00.5531477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5533677Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5535585Z         
2025-03-14T05:10:00.5535955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5536448Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:10:00.5536722Z         
2025-03-14T05:10:00.5537093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5537590Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:10:00.5537863Z         
2025-03-14T05:10:00.5538487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5539327Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5539940Z         
2025-03-14T05:10:00.5540299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5542928Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5544961Z         
2025-03-14T05:10:00.5545369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5545899Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:10:00.5546170Z         
2025-03-14T05:10:00.5546517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5547331Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5547942Z         
2025-03-14T05:10:00.5548299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5550443Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5552291Z         
2025-03-14T05:10:00.5552657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5553133Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:10:00.5553396Z         
2025-03-14T05:10:00.5553726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5554520Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5555165Z         
2025-03-14T05:10:00.5555525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5557576Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5559453Z         
2025-03-14T05:10:00.5559820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5560297Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:10:00.5560556Z         
2025-03-14T05:10:00.5560919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5561404Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:10:00.5561670Z         
2025-03-14T05:10:00.5562005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5562814Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5563407Z         
2025-03-14T05:10:00.5563752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5566219Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5568109Z         
2025-03-14T05:10:00.5568493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5568985Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:10:00.5569283Z         
2025-03-14T05:10:00.5569620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5570418Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5571036Z         
2025-03-14T05:10:00.5571382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5573427Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5575263Z         
2025-03-14T05:10:00.5575631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5576111Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:10:00.5576372Z         
2025-03-14T05:10:00.5576701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5577568Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5578596Z         
2025-03-14T05:10:00.5578991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5581786Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5583709Z         
2025-03-14T05:10:00.5584068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5585089Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.5585782Z         
2025-03-14T05:10:00.5586141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5588309Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5590414Z         
2025-03-14T05:10:00.5590784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5591275Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:10:00.5591545Z         
2025-03-14T05:10:00.5591914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5592461Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:10:00.5592739Z         
2025-03-14T05:10:00.5593086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5593900Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5594511Z         
2025-03-14T05:10:00.5594868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5596986Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5598900Z         
2025-03-14T05:10:00.5599279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5599772Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:10:00.5600058Z         
2025-03-14T05:10:00.5600399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5601206Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5601814Z         
2025-03-14T05:10:00.5602167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5604759Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5606665Z         
2025-03-14T05:10:00.5607108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5607603Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:10:00.5607874Z         
2025-03-14T05:10:00.5608215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5609073Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5609699Z         
2025-03-14T05:10:00.5610051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5612157Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5614127Z         
2025-03-14T05:10:00.5614540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5615024Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:10:00.5615300Z         
2025-03-14T05:10:00.5615661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5616153Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:10:00.5616425Z         
2025-03-14T05:10:00.5616759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5619254Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5619905Z         
2025-03-14T05:10:00.5620252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5622419Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5624416Z         
2025-03-14T05:10:00.5626457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5627012Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:10:00.5627289Z         
2025-03-14T05:10:00.5627637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5628451Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5629065Z         
2025-03-14T05:10:00.5629422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5631522Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5633539Z         
2025-03-14T05:10:00.5633924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5634436Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:10:00.5634710Z         
2025-03-14T05:10:00.5635061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5635900Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5636529Z         
2025-03-14T05:10:00.5636891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5639111Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5641039Z         
2025-03-14T05:10:00.5641426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5641906Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:10:00.5642180Z         
2025-03-14T05:10:00.5642547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5643027Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:10:00.5643293Z         
2025-03-14T05:10:00.5643622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5644393Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5645002Z         
2025-03-14T05:10:00.5645347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5647421Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5649296Z         
2025-03-14T05:10:00.5649661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5650168Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:10:00.5650433Z         
2025-03-14T05:10:00.5650773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5651607Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5652202Z         
2025-03-14T05:10:00.5652587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5654643Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5656472Z         
2025-03-14T05:10:00.5656839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5657327Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:10:00.5657594Z         
2025-03-14T05:10:00.5657934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5658842Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5659485Z         
2025-03-14T05:10:00.5659842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5661964Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5663841Z         
2025-03-14T05:10:00.5664300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5664832Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:10:00.5665121Z         
2025-03-14T05:10:00.5665508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5666002Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:10:00.5666279Z         
2025-03-14T05:10:00.5666689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5667493Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5668094Z         
2025-03-14T05:10:00.5668451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5670545Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5672431Z         
2025-03-14T05:10:00.5672797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5673280Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:10:00.5673560Z         
2025-03-14T05:10:00.5673899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5674687Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5675287Z         
2025-03-14T05:10:00.5675622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5677656Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5679468Z         
2025-03-14T05:10:00.5679862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5680330Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:10:00.5680581Z         
2025-03-14T05:10:00.5680911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5681865Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5682468Z         
2025-03-14T05:10:00.5682815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5684868Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5688771Z         
2025-03-14T05:10:00.5689186Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5690055Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.5690677Z         
2025-03-14T05:10:00.5691037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5693190Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5695369Z         
2025-03-14T05:10:00.5695746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5696290Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:10:00.5696559Z         
2025-03-14T05:10:00.5696931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5697417Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:10:00.5697676Z         
2025-03-14T05:10:00.5698057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5698876Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5699476Z         
2025-03-14T05:10:00.5699831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5701920Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5703841Z         
2025-03-14T05:10:00.5704356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5704907Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:10:00.5705202Z         
2025-03-14T05:10:00.5705564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5706385Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5706999Z         
2025-03-14T05:10:00.5707358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5709477Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5711333Z         
2025-03-14T05:10:00.5711714Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5712200Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:10:00.5712454Z         
2025-03-14T05:10:00.5712784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5713566Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5714153Z         
2025-03-14T05:10:00.5714497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5716563Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5718440Z         
2025-03-14T05:10:00.5719573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5720113Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:10:00.5720381Z         
2025-03-14T05:10:00.5720755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5721230Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:10:00.5721491Z         
2025-03-14T05:10:00.5721826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5722601Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5723191Z         
2025-03-14T05:10:00.5723536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5725667Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5727477Z         
2025-03-14T05:10:00.5727847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5728315Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:10:00.5728567Z         
2025-03-14T05:10:00.5728900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5732476Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5733262Z         
2025-03-14T05:10:00.5733616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5737714Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5739763Z         
2025-03-14T05:10:00.5740181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5740677Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:10:00.5740946Z         
2025-03-14T05:10:00.5741301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5742116Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5742729Z         
2025-03-14T05:10:00.5743096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5745328Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5747224Z         
2025-03-14T05:10:00.5747611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5748114Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:10:00.5748384Z         
2025-03-14T05:10:00.5748764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5749255Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:10:00.5749511Z         
2025-03-14T05:10:00.5749856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5750686Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5751297Z         
2025-03-14T05:10:00.5751651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5753736Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5755540Z         
2025-03-14T05:10:00.5755902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5756367Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:10:00.5756618Z         
2025-03-14T05:10:00.5756948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5757751Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5758346Z         
2025-03-14T05:10:00.5758690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5760714Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5762546Z         
2025-03-14T05:10:00.5762912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5763380Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:10:00.5763649Z         
2025-03-14T05:10:00.5763982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5764765Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5765366Z         
2025-03-14T05:10:00.5765711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5767744Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5769558Z         
2025-03-14T05:10:00.5769916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5770389Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:10:00.5770646Z         
2025-03-14T05:10:00.5771038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5771507Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:10:00.5771759Z         
2025-03-14T05:10:00.5772084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5772844Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5773422Z         
2025-03-14T05:10:00.5773761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5775780Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5777631Z         
2025-03-14T05:10:00.5778002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5778482Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:10:00.5778735Z         
2025-03-14T05:10:00.5779066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5779839Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5780423Z         
2025-03-14T05:10:00.5780769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5783119Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5785094Z         
2025-03-14T05:10:00.5785496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5786020Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:10:00.5786293Z         
2025-03-14T05:10:00.5786640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5787444Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5788052Z         
2025-03-14T05:10:00.5788407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5790505Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5792430Z         
2025-03-14T05:10:00.5792796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5793283Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:10:00.5793547Z         
2025-03-14T05:10:00.5793920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5794404Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:10:00.5794668Z         
2025-03-14T05:10:00.5795003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5795789Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5796390Z         
2025-03-14T05:10:00.5796742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5798890Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5800708Z         
2025-03-14T05:10:00.5801068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5801533Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:10:00.5801787Z         
2025-03-14T05:10:00.5802120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5802899Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5803499Z         
2025-03-14T05:10:00.5803840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5805887Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5807734Z         
2025-03-14T05:10:00.5808098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5808563Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:10:00.5808818Z         
2025-03-14T05:10:00.5809155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5809928Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5810521Z         
2025-03-14T05:10:00.5810870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5812932Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5814763Z         
2025-03-14T05:10:00.5815123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5815594Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:10:00.5815855Z         
2025-03-14T05:10:00.5816216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5816687Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:10:00.5816942Z         
2025-03-14T05:10:00.5817262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5818037Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5818631Z         
2025-03-14T05:10:00.5818976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5830209Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5832135Z         
2025-03-14T05:10:00.5832525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5833008Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:10:00.5833275Z         
2025-03-14T05:10:00.5833622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5834577Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5835203Z         
2025-03-14T05:10:00.5835560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5837655Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5839527Z         
2025-03-14T05:10:00.5839894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5840355Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:10:00.5840605Z         
2025-03-14T05:10:00.5840929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5841731Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5842328Z         
2025-03-14T05:10:00.5842673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5844713Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5846558Z         
2025-03-14T05:10:00.5846920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5847399Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:10:00.5847664Z         
2025-03-14T05:10:00.5848023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5848526Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:10:00.5848791Z         
2025-03-14T05:10:00.5849128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5849898Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5850473Z         
2025-03-14T05:10:00.5850822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5852856Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5854707Z         
2025-03-14T05:10:00.5855074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5855549Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:10:00.5855816Z         
2025-03-14T05:10:00.5856145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5856924Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5857512Z         
2025-03-14T05:10:00.5857860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5860006Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5861876Z         
2025-03-14T05:10:00.5862284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5862760Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:10:00.5863020Z         
2025-03-14T05:10:00.5863353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5864202Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5864821Z         
2025-03-14T05:10:00.5865177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5867289Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5869169Z         
2025-03-14T05:10:00.5869543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5870418Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:10:00.5870483Z         
2025-03-14T05:10:00.5870775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5870915Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:10:00.5870987Z         
2025-03-14T05:10:00.5871238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5871725Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5871793Z         
2025-03-14T05:10:00.5872067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5873853Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5873930Z         
2025-03-14T05:10:00.5874227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5874368Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:10:00.5874441Z         
2025-03-14T05:10:00.5874691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5875184Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5875252Z         
2025-03-14T05:10:00.5875521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5877270Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5877367Z         
2025-03-14T05:10:00.5877653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5877790Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:10:00.5877859Z         
2025-03-14T05:10:00.5878107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5878585Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5878651Z         
2025-03-14T05:10:00.5878915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5880670Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5880738Z         
2025-03-14T05:10:00.5881018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5881168Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:10:00.5881242Z         
2025-03-14T05:10:00.5881662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5881819Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:10:00.5881884Z         
2025-03-14T05:10:00.5882138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5882618Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5882741Z         
2025-03-14T05:10:00.5883007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5884742Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5884840Z         
2025-03-14T05:10:00.5885123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5885258Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:10:00.5885329Z         
2025-03-14T05:10:00.5885570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5886046Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5886114Z         
2025-03-14T05:10:00.5886430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5888147Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5888222Z         
2025-03-14T05:10:00.5888510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5888642Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:10:00.5888714Z         
2025-03-14T05:10:00.5888958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5889440Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5889522Z         
2025-03-14T05:10:00.5889805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5891533Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5891599Z         
2025-03-14T05:10:00.5891879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5892025Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:10:00.5892094Z         
2025-03-14T05:10:00.5892371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5892520Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:10:00.5892582Z         
2025-03-14T05:10:00.5892858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5893333Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5893406Z         
2025-03-14T05:10:00.5893662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5895413Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5895502Z         
2025-03-14T05:10:00.5895782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5895924Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:10:00.5895993Z         
2025-03-14T05:10:00.5896265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5896749Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5896812Z         
2025-03-14T05:10:00.5897074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5898802Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5898877Z         
2025-03-14T05:10:00.5899161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5899321Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:10:00.5899396Z         
2025-03-14T05:10:00.5899645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5900141Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5900208Z         
2025-03-14T05:10:00.5900481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5902267Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5902356Z         
2025-03-14T05:10:00.5902642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5902813Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:10:00.5902885Z         
2025-03-14T05:10:00.5903164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5903321Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:10:00.5905341Z         
2025-03-14T05:10:00.5905705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5906215Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5906294Z         
2025-03-14T05:10:00.5906558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5908407Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5908486Z         
2025-03-14T05:10:00.5908770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5908918Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:10:00.5908983Z         
2025-03-14T05:10:00.5909245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5909740Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5909817Z         
2025-03-14T05:10:00.5910079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5911876Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5911987Z         
2025-03-14T05:10:00.5912269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5912412Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:10:00.5912479Z         
2025-03-14T05:10:00.5912735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5913228Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5913295Z         
2025-03-14T05:10:00.5913565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5915381Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5915461Z         
2025-03-14T05:10:00.5915752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5915902Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:10:00.5915976Z         
2025-03-14T05:10:00.5916262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5916409Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:10:00.5916474Z         
2025-03-14T05:10:00.5916729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5917212Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5917337Z         
2025-03-14T05:10:00.5917598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5919359Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5919451Z         
2025-03-14T05:10:00.5919729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5919890Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:10:00.5919952Z         
2025-03-14T05:10:00.5920199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5921353Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5921448Z         
2025-03-14T05:10:00.5921770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5923514Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5923591Z         
2025-03-14T05:10:00.5923875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5924016Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:10:00.5924080Z         
2025-03-14T05:10:00.5924331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5924811Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5924899Z         
2025-03-14T05:10:00.5925157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5926905Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5926978Z         
2025-03-14T05:10:00.5927252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5927408Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:10:00.5927471Z         
2025-03-14T05:10:00.5927757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5927904Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:10:00.5927967Z         
2025-03-14T05:10:00.5928247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5928715Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5928790Z         
2025-03-14T05:10:00.5929048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5932969Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5933077Z         
2025-03-14T05:10:00.5933432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5933581Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:10:00.5933647Z         
2025-03-14T05:10:00.5933921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5934396Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5934468Z         
2025-03-14T05:10:00.5934723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5936462Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5936538Z         
2025-03-14T05:10:00.5936816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5936989Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:10:00.5937053Z         
2025-03-14T05:10:00.5937302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5937770Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5937840Z         
2025-03-14T05:10:00.5938157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5939925Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5940013Z         
2025-03-14T05:10:00.5940288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5940456Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:10:00.5940520Z         
2025-03-14T05:10:00.5940804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5940942Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:10:00.5941014Z         
2025-03-14T05:10:00.5941256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5941739Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5941810Z         
2025-03-14T05:10:00.5942067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5943833Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5943912Z         
2025-03-14T05:10:00.5944251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5944404Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:10:00.5944469Z         
2025-03-14T05:10:00.5944732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5945225Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5945300Z         
2025-03-14T05:10:00.5945564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5947366Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5947481Z         
2025-03-14T05:10:00.5947765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5947910Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:10:00.5947976Z         
2025-03-14T05:10:00.5948236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5948729Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5948804Z         
2025-03-14T05:10:00.5949067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5950885Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5950962Z         
2025-03-14T05:10:00.5951243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5951400Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:10:00.5951465Z         
2025-03-14T05:10:00.5951756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5951896Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:10:00.5951969Z         
2025-03-14T05:10:00.5952215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5952702Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5952783Z         
2025-03-14T05:10:00.5953055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5954840Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5954919Z         
2025-03-14T05:10:00.5955218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5955358Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:10:00.5955431Z         
2025-03-14T05:10:00.5955680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5956173Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5956249Z         
2025-03-14T05:10:00.5956550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5958340Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5958418Z         
2025-03-14T05:10:00.5958696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5958835Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:10:00.5958898Z         
2025-03-14T05:10:00.5959148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5959622Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5959710Z         
2025-03-14T05:10:00.5959967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5961718Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5961792Z         
2025-03-14T05:10:00.5962062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5962213Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:10:00.5962276Z         
2025-03-14T05:10:00.5962554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5962692Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:10:00.5962762Z         
2025-03-14T05:10:00.5963029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5963500Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5963564Z         
2025-03-14T05:10:00.5963825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5965546Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5965611Z         
2025-03-14T05:10:00.5965891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5966039Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:10:00.5966109Z         
2025-03-14T05:10:00.5966354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5966845Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5966910Z         
2025-03-14T05:10:00.5967169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5968913Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5968979Z         
2025-03-14T05:10:00.5969261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5969426Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:10:00.5969498Z         
2025-03-14T05:10:00.5969740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5970218Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5970288Z         
2025-03-14T05:10:00.5970543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5972261Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5972351Z         
2025-03-14T05:10:00.5972630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5972796Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:10:00.5972860Z         
2025-03-14T05:10:00.5973140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5973276Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:10:00.5973346Z         
2025-03-14T05:10:00.5973586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5974060Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5974123Z         
2025-03-14T05:10:00.5974386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5976141Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5976218Z         
2025-03-14T05:10:00.5976505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5976639Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:10:00.5976709Z         
2025-03-14T05:10:00.5976949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5977434Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5977499Z         
2025-03-14T05:10:00.5977762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5979501Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5979594Z         
2025-03-14T05:10:00.5979883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5980016Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:10:00.5980088Z         
2025-03-14T05:10:00.5980332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5980819Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5980884Z         
2025-03-14T05:10:00.5981150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5983187Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5983267Z         
2025-03-14T05:10:00.5983558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5983708Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:10:00.5983784Z         
2025-03-14T05:10:00.5984065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5984267Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:10:00.5984345Z         
2025-03-14T05:10:00.5984596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5985093Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5985189Z         
2025-03-14T05:10:00.5985462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5987240Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5987339Z         
2025-03-14T05:10:00.5987635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5987785Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:10:00.5987859Z         
2025-03-14T05:10:00.5988108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5988615Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5988681Z         
2025-03-14T05:10:00.5988986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5990772Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5990841Z         
2025-03-14T05:10:00.5991134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5991279Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:10:00.5991350Z         
2025-03-14T05:10:00.5991600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5992104Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.5992185Z         
2025-03-14T05:10:00.5992457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5994247Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5994314Z         
2025-03-14T05:10:00.5994601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.5994753Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:10:00.5994828Z         
2025-03-14T05:10:00.5995110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5995267Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:10:00.5995331Z         
2025-03-14T05:10:00.5995617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5996096Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.5996168Z         
2025-03-14T05:10:00.5996434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.5998211Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.5998284Z         
2025-03-14T05:10:00.5998570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.5998724Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:10:00.5998795Z         
2025-03-14T05:10:00.5999044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.5999556Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.5999620Z         
2025-03-14T05:10:00.5999886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6001646Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6001721Z         
2025-03-14T05:10:00.6002009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6002179Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:10:00.6002253Z         
2025-03-14T05:10:00.6002501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6003000Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6003065Z         
2025-03-14T05:10:00.6003334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6005120Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6005203Z         
2025-03-14T05:10:00.6005497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6005683Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:10:00.6005757Z         
2025-03-14T05:10:00.6006038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6006193Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:10:00.6006258Z         
2025-03-14T05:10:00.6006511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6006997Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6007073Z         
2025-03-14T05:10:00.6007333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6009143Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6009221Z         
2025-03-14T05:10:00.6009506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6009651Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:10:00.6009716Z         
2025-03-14T05:10:00.6009976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6011698Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6011797Z         
2025-03-14T05:10:00.6012086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6013908Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6014045Z         
2025-03-14T05:10:00.6014344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6014483Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:10:00.6014557Z         
2025-03-14T05:10:00.6014808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6015320Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6015388Z         
2025-03-14T05:10:00.6015667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6017527Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6017607Z         
2025-03-14T05:10:00.6017902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6018131Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:10:00.6018212Z         
2025-03-14T05:10:00.6018508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6018666Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:10:00.6018733Z         
2025-03-14T05:10:00.6018995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6019491Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6019586Z         
2025-03-14T05:10:00.6019858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6022608Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6022739Z         
2025-03-14T05:10:00.6023967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6024296Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:10:00.6024372Z         
2025-03-14T05:10:00.6024675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6025203Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6025292Z         
2025-03-14T05:10:00.6025654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6027511Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6027589Z         
2025-03-14T05:10:00.6027875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6028021Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:10:00.6028087Z         
2025-03-14T05:10:00.6028343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6028850Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6028932Z         
2025-03-14T05:10:00.6029202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6030997Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6031074Z         
2025-03-14T05:10:00.6031359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6031515Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:10:00.6031587Z         
2025-03-14T05:10:00.6031866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6032017Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:10:00.6032082Z         
2025-03-14T05:10:00.6032372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6032895Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6032972Z         
2025-03-14T05:10:00.6033233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6035007Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6035082Z         
2025-03-14T05:10:00.6035398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6035543Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:10:00.6035609Z         
2025-03-14T05:10:00.6035863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6036357Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6036432Z         
2025-03-14T05:10:00.6036691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6038467Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6038544Z         
2025-03-14T05:10:00.6038823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6038996Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:10:00.6039065Z         
2025-03-14T05:10:00.6039320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6039804Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6039878Z         
2025-03-14T05:10:00.6040139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6041935Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6042025Z         
2025-03-14T05:10:00.6042314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6042497Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:10:00.6042562Z         
2025-03-14T05:10:00.6042846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6042999Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:10:00.6043063Z         
2025-03-14T05:10:00.6043316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6043794Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6043866Z         
2025-03-14T05:10:00.6044119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6045951Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6046026Z         
2025-03-14T05:10:00.6046311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6046455Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T05:10:00.6046518Z         
2025-03-14T05:10:00.6046777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6047262Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6047335Z         
2025-03-14T05:10:00.6047600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6049362Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6049465Z         
2025-03-14T05:10:00.6049747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6049890Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T05:10:00.6049955Z         
2025-03-14T05:10:00.6050210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6050697Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6050771Z         
2025-03-14T05:10:00.6051035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6052808Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6052886Z         
2025-03-14T05:10:00.6053130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6053610Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.6053677Z         
2025-03-14T05:10:00.6053940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6055733Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6055836Z         
2025-03-14T05:10:00.6056118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6056265Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T05:10:00.6056338Z         
2025-03-14T05:10:00.6056612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6056760Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T05:10:00.6056831Z         
2025-03-14T05:10:00.6057075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6057545Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6057609Z         
2025-03-14T05:10:00.6057872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6059699Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6059775Z         
2025-03-14T05:10:00.6060066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6060200Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T05:10:00.6060272Z         
2025-03-14T05:10:00.6060520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6061008Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6061073Z         
2025-03-14T05:10:00.6061340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6063114Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6063198Z         
2025-03-14T05:10:00.6063486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6063622Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T05:10:00.6063695Z         
2025-03-14T05:10:00.6063953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6064565Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6064645Z         
2025-03-14T05:10:00.6064928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6066763Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6066833Z         
2025-03-14T05:10:00.6067122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6067280Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T05:10:00.6067358Z         
2025-03-14T05:10:00.6067638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6067791Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T05:10:00.6067856Z         
2025-03-14T05:10:00.6068114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6068603Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6068695Z         
2025-03-14T05:10:00.6068984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6070775Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6070851Z         
2025-03-14T05:10:00.6071134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6071276Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T05:10:00.6071352Z         
2025-03-14T05:10:00.6071599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6072118Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6072186Z         
2025-03-14T05:10:00.6072456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6074214Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6074290Z         
2025-03-14T05:10:00.6074581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6074713Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T05:10:00.6074804Z         
2025-03-14T05:10:00.6075050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6075567Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6075655Z         
2025-03-14T05:10:00.6075940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6090287Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6090542Z         
2025-03-14T05:10:00.6090927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6091123Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T05:10:00.6091198Z         
2025-03-14T05:10:00.6091766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6091978Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T05:10:00.6092065Z         
2025-03-14T05:10:00.6092366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6093075Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.6093164Z         
2025-03-14T05:10:00.6093445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6094028Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.6094100Z         
2025-03-14T05:10:00.6094544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.6094831Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T05:10:00.6094955Z         
2025-03-14T05:10:00.6095220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6095844Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.6095913Z         
2025-03-14T05:10:00.6096409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.6096695Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T05:10:00.6096821Z         
2025-03-14T05:10:00.6097203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6097982Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.6098105Z         
2025-03-14T05:10:00.6098646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.6099000Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:10:00.6099072Z         
2025-03-14T05:10:00.6099421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6100020Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.6100101Z         
2025-03-14T05:10:00.6100459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.6100689Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T05:10:00.6100759Z         
2025-03-14T05:10:00.6101031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6101633Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.6101702Z         
2025-03-14T05:10:00.6102121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.6102485Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:10:00.6102562Z         
2025-03-14T05:10:00.6102824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6103450Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.6103522Z         
2025-03-14T05:10:00.6103889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.6104215Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T05:10:00.6104317Z         
2025-03-14T05:10:00.6104586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6105226Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.6105307Z         
2025-03-14T05:10:00.6105687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:10:00.6105917Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:10:00.6105988Z         
2025-03-14T05:10:00.6133594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6133900Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.6133992Z         
2025-03-14T05:10:00.6134331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6134491Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:00.6134571Z         
2025-03-14T05:10:00.6135046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6135206Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.6135283Z         
2025-03-14T05:10:00.6135583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6135740Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:10:00.6135811Z         
2025-03-14T05:10:00.6136194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.6136413Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:10:00.6136524Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:10:00.6136653Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:10:00.6136726Z         
2025-03-14T05:10:00.6137083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.6137225Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:10:00.6137292Z         
2025-03-14T05:10:00.6137680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.6137813Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:10:00.6137896Z         
2025-03-14T05:10:00.6138612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.6138896Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:10:00.6138974Z         
2025-03-14T05:10:00.6139428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.6139570Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:10:00.6140242Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:10:00.6140408Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:10:00.6140571Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:10:00.6140653Z         
2025-03-14T05:10:00.6141165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6141336Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.6141404Z         
2025-03-14T05:10:00.6141717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6141862Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:10:00.6141938Z         
2025-03-14T05:10:00.6142380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6142544Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.6142611Z         
2025-03-14T05:10:00.6142920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6143066Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:10:00.6143141Z         
2025-03-14T05:10:00.6143519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.6143755Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:10:00.6164859Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:10:00.6165192Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:10:00.6165377Z         
2025-03-14T05:10:00.6165782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.6165924Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:10:00.6166001Z         
2025-03-14T05:10:00.6166347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.6166485Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:10:00.6166555Z         
2025-03-14T05:10:00.6166961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.6167186Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:10:00.6167262Z         
2025-03-14T05:10:00.6167702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.6167839Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:10:00.6168274Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:10:00.6168407Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:10:00.6168583Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:10:00.6168651Z         
2025-03-14T05:10:00.6169718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6169909Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.6169987Z         
2025-03-14T05:10:00.6170306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6170463Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:10:00.6170529Z         
2025-03-14T05:10:00.6170981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6171136Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.6171208Z         
2025-03-14T05:10:00.6171507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6171654Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:10:00.6171719Z         
2025-03-14T05:10:00.6172094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.6172321Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:10:00.6172431Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:10:00.6172559Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:10:00.6172663Z         
2025-03-14T05:10:00.6172994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.6173130Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:10:00.6173197Z         
2025-03-14T05:10:00.6173531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.6173657Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:10:00.6173733Z         
2025-03-14T05:10:00.6174692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.6174920Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:10:00.6174987Z         
2025-03-14T05:10:00.6175412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.6175549Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:10:00.6175967Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:10:00.6176102Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:10:00.6176260Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:10:00.6176335Z         
2025-03-14T05:10:00.6176769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6176925Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.6176991Z         
2025-03-14T05:10:00.6177289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6177431Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:10:00.6177505Z         
2025-03-14T05:10:00.6177932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6178612Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.6178693Z         
2025-03-14T05:10:00.6179017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6179159Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:10:00.6179246Z         
2025-03-14T05:10:00.6179628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.6179853Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:10:00.6179968Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:10:00.6180106Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:10:00.6180178Z         
2025-03-14T05:10:00.6180501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.6180631Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:10:00.6180695Z         
2025-03-14T05:10:00.6181020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.6181143Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:10:00.6181218Z         
2025-03-14T05:10:00.6181775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.6182004Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:10:00.6182069Z         
2025-03-14T05:10:00.6182492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.6182622Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:10:00.6183051Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:10:00.6183287Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:10:00.6183409Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:10:00.6183482Z         
2025-03-14T05:10:00.6183916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6184073Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.6184260Z         
2025-03-14T05:10:00.6184577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6184721Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:10:00.6208643Z         
2025-03-14T05:10:00.6209389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.6209584Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.6209656Z         
2025-03-14T05:10:00.6209983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6210137Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:10:00.6210214Z         
2025-03-14T05:10:00.6210760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.6210972Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:10:00.6211121Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:10:00.6211258Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:10:00.6211329Z         
2025-03-14T05:10:00.6211670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.6211801Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:10:00.6211872Z         
2025-03-14T05:10:00.6212200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.6212332Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:10:00.6212398Z         
2025-03-14T05:10:00.6212791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.6213008Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:10:00.6213084Z         
2025-03-14T05:10:00.6213508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.6213644Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:10:00.6214068Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:10:00.6214247Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:10:00.6214377Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:10:00.6214442Z         
2025-03-14T05:10:00.6214756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.6214887Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T05:10:00.6215022Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T05:10:00.6215143Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T05:10:00.6215275Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T05:10:00.6215395Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T05:10:00.6215467Z         
2025-03-14T05:10:00.6215737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6216255Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T05:10:00.6216325Z         
2025-03-14T05:10:00.6216613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.6216831Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T05:10:00.6216905Z         
2025-03-14T05:10:00.6217289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.6217833Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.6217900Z         
2025-03-14T05:10:00.6218362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.6218889Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T05:10:00.6218967Z         
2025-03-14T05:10:00.6219233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6219713Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T05:10:00.6219786Z         
2025-03-14T05:10:00.6220071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.6220269Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T05:10:00.6220335Z         
2025-03-14T05:10:00.6220775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.6221276Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.6221348Z         
2025-03-14T05:10:00.6221691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.6222196Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T05:10:00.6222263Z         
2025-03-14T05:10:00.6222514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6222982Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T05:10:00.6224601Z         
2025-03-14T05:10:00.6224975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.6225246Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T05:10:00.6225320Z         
2025-03-14T05:10:00.6225704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.6226234Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.6226301Z         
2025-03-14T05:10:00.6226660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.6227158Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T05:10:00.6227236Z         
2025-03-14T05:10:00.6227485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6227955Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T05:10:00.6228027Z         
2025-03-14T05:10:00.6228297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.6228486Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T05:10:00.6228552Z         
2025-03-14T05:10:00.6229002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.6229496Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.6229571Z         
2025-03-14T05:10:00.6229921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.6230425Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T05:10:00.6230491Z         
2025-03-14T05:10:00.6230749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6231501Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:10:00.6231593Z         
2025-03-14T05:10:00.6231872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.6232051Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T05:10:00.6232139Z         
2025-03-14T05:10:00.6232516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.6254064Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:10:00.6254177Z         
2025-03-14T05:10:00.6254541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.6255362Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:10:00.6255438Z         
2025-03-14T05:10:00.6255777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:10:00.6255954Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:10:00.6256139Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:10:00.6256317Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:10:00.6256465Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:10:00.6256628Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:10:00.6256770Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:10:00.6256922Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:10:00.6257073Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:10:00.6257230Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:10:00.6257388Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:10:00.6257457Z         
2025-03-14T05:10:00.6257893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:10:00.6258358Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T05:10:00.6258592Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:10:00.6258815Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:10:00.6258991Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T05:10:00.6259172Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:10:00.6259369Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:10:00.6259524Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T05:10:00.6259698Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:10:00.6259871Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:10:00.6260014Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T05:10:00.6260181Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:10:00.6260350Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:10:00.6260496Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T05:10:00.6260650Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:10:00.6260817Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:10:00.6260883Z         
2025-03-14T05:10:00.6261310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.6261517Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:10:00.6261587Z         
2025-03-14T05:10:00.6262049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.6262216Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:10:00.6262367Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:00.6262513Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:00.6262577Z         
2025-03-14T05:10:00.6262959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.6263129Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:00.6263207Z         
2025-03-14T05:10:00.6263516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.6263669Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:00.6263736Z         
2025-03-14T05:10:00.6264056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.6264282Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:00.6264449Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6264612Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:00.6264686Z         
2025-03-14T05:10:00.6265025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.6265178Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:00.6265302Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:00.6265462Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:10:00.6265527Z         
2025-03-14T05:10:00.6265844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.6265980Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6266072Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:00.6266210Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:10:00.6266275Z         
2025-03-14T05:10:00.6266597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.6266748Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:00.6266846Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:00.6266974Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:10:00.6267045Z         
2025-03-14T05:10:00.6267374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.6267538Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.6267654Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:10:00.6267724Z         
2025-03-14T05:10:00.6268068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.6268233Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.6268347Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:10:00.6268420Z         
2025-03-14T05:10:00.6268718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.6268875Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.6268989Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:10:00.6269061Z         
2025-03-14T05:10:00.6269364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.6269555Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:00.6269667Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:10:00.6269737Z         
2025-03-14T05:10:00.6270080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.6270233Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:00.6270315Z         
2025-03-14T05:10:00.6270658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.6270801Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:00.6270900Z         
2025-03-14T05:10:00.6271247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.6271396Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:00.6271523Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:10:00.6271682Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:00.6271824Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:10:00.6271900Z         
2025-03-14T05:10:00.6272247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.6272400Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:00.6272524Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:10:00.6272683Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:00.6272823Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:10:00.6272896Z         
2025-03-14T05:10:00.6273229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.6273359Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:00.6274219Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:00.6274803Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:10:00.6275809Z         
2025-03-14T05:10:00.6277056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.6277543Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:00.6277815Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:00.6278691Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:10:00.6278798Z         
2025-03-14T05:10:00.6279176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.6279284Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:00.6279424Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:00.6279496Z         
2025-03-14T05:10:00.6279818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.6279913Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:00.6280039Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:00.6280105Z         
2025-03-14T05:10:00.6280464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.6280625Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:00.6280767Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:00.6280833Z         
2025-03-14T05:10:00.6281148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.6281281Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:00.6281415Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:00.6281643Z         
2025-03-14T05:10:00.6282007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.6282195Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:00.6282273Z         
2025-03-14T05:10:00.6282608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.6282783Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:00.6282851Z         
2025-03-14T05:10:00.6283240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.6283416Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:00.6283494Z         
2025-03-14T05:10:00.6283889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.6284111Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:00.6284176Z         
2025-03-14T05:10:00.6284707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.6284869Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:10:00.6285030Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:00.6285182Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:00.6285248Z         
2025-03-14T05:10:00.6285632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.6285807Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:00.6285883Z         
2025-03-14T05:10:00.6286204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.6286367Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:00.6286433Z         
2025-03-14T05:10:00.6286759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.6286896Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:00.6287059Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6287215Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:10:00.6287289Z         
2025-03-14T05:10:00.6287607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.6287764Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:00.6287907Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:00.6288071Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:10:00.6288139Z         
2025-03-14T05:10:00.6288454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.6288580Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6288688Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:00.6288824Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:10:00.6288900Z         
2025-03-14T05:10:00.6290367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.6290563Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:00.6290663Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:00.6290800Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:10:00.6290867Z         
2025-03-14T05:10:00.6291194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.6291356Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.6291479Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:10:00.6291593Z         
2025-03-14T05:10:00.6291905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.6292060Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.6292182Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:10:00.6292249Z         
2025-03-14T05:10:00.6292553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.6292704Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.6292822Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:10:00.6292887Z         
2025-03-14T05:10:00.6293197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.6293384Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:00.6293507Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:10:00.6293570Z         
2025-03-14T05:10:00.6293914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.6294057Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:00.6294146Z         
2025-03-14T05:10:00.6294478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.6294630Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:00.6294712Z         
2025-03-14T05:10:00.6295063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.6295205Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.6295346Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:10:00.6295504Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:00.6295662Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:10:00.6295728Z         
2025-03-14T05:10:00.6296083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.6296226Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.6296358Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:10:00.6296515Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:00.6296670Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:10:00.6296735Z         
2025-03-14T05:10:00.6297067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.6297187Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:00.6297416Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:00.6297556Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:10:00.6297630Z         
2025-03-14T05:10:00.6297956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.6298139Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:00.6298317Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:00.6298458Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:10:00.6298527Z         
2025-03-14T05:10:00.6298843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.6298949Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:00.6299084Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:00.6299148Z         
2025-03-14T05:10:00.6299463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.6299561Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:00.6299687Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:00.6299751Z         
2025-03-14T05:10:00.6300065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.6300228Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:00.6300382Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:00.6300471Z         
2025-03-14T05:10:00.6300780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.6302162Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:00.6302396Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:00.6302481Z         
2025-03-14T05:10:00.6302851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.6303066Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:00.6303133Z         
2025-03-14T05:10:00.6303484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.6303656Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:00.6303731Z         
2025-03-14T05:10:00.6304197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.6304396Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:00.6304473Z         
2025-03-14T05:10:00.6304909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.6305186Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:00.6305260Z         
2025-03-14T05:10:00.6305728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.6305893Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:10:00.6306064Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:00.6306209Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:00.6306288Z         
2025-03-14T05:10:00.6306680Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.6306867Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:10:00.6306942Z         
2025-03-14T05:10:00.6307280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.6307447Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:00.6307516Z         
2025-03-14T05:10:00.6307853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.6308015Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:00.6308157Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6308317Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:10:00.6308400Z         
2025-03-14T05:10:00.6308752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.6308891Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:00.6309018Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:00.6309186Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:10:00.6309255Z         
2025-03-14T05:10:00.6309593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.6309722Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6309827Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:00.6309969Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:10:00.6310051Z         
2025-03-14T05:10:00.6310381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.6310544Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:00.6310656Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:00.6310795Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:10:00.6310873Z         
2025-03-14T05:10:00.6311231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.6311407Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.6311571Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:10:00.6312100Z         
2025-03-14T05:10:00.6312881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.6314852Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.6314986Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:10:00.6315055Z         
2025-03-14T05:10:00.6315381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.6315555Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.6315667Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:10:00.6315743Z         
2025-03-14T05:10:00.6316040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.6316233Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:00.6316342Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:10:00.6316415Z         
2025-03-14T05:10:00.6316743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.6316905Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:00.6316969Z         
2025-03-14T05:10:00.6317303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.6317453Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:00.6317525Z         
2025-03-14T05:10:00.6317855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.6317994Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.6318114Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:10:00.6318276Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:00.6318414Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:10:00.6318487Z         
2025-03-14T05:10:00.6318820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.6318963Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.6319081Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:10:00.6319237Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:00.6319371Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:10:00.6319447Z         
2025-03-14T05:10:00.6319768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.6319893Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:00.6320086Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:00.6320233Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:10:00.6320297Z         
2025-03-14T05:10:00.6320622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.6320743Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:00.6320907Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:00.6321048Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:10:00.6321114Z         
2025-03-14T05:10:00.6321428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.6321527Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:00.6321653Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:00.6321718Z         
2025-03-14T05:10:00.6322025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.6322121Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:00.6322243Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:00.6322324Z         
2025-03-14T05:10:00.6322630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.6322744Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:00.6322883Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:00.6322975Z         
2025-03-14T05:10:00.6323280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.6323395Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:00.6323533Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:00.6323599Z         
2025-03-14T05:10:00.6324725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.6325033Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:00.6325114Z         
2025-03-14T05:10:00.6325463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.6325627Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:00.6325703Z         
2025-03-14T05:10:00.6326080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.6326262Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:00.6326330Z         
2025-03-14T05:10:00.6326728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.6326992Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:10:00.6327070Z         
2025-03-14T05:10:00.6327497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.6327654Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:10:00.6327803Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:00.6327954Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:00.6328020Z         
2025-03-14T05:10:00.6328394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.6328562Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:10:00.6328639Z         
2025-03-14T05:10:00.6328942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.6329094Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:00.6329159Z         
2025-03-14T05:10:00.6329470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.6329618Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:00.6329748Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6329897Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:10:00.6329992Z         
2025-03-14T05:10:00.6330303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.6330435Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:00.6330554Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:00.6330712Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:10:00.6330780Z         
2025-03-14T05:10:00.6331085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.6331215Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6331312Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:00.6331452Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:10:00.6331517Z         
2025-03-14T05:10:00.6331827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.6331972Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:00.6332074Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:00.6332201Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:10:00.6332279Z         
2025-03-14T05:10:00.6332584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.6332787Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.6332903Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:10:00.6332979Z         
2025-03-14T05:10:00.6333280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.6333434Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.6333546Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:10:00.6333623Z         
2025-03-14T05:10:00.6333920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.6334077Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.6334203Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:10:00.6334279Z         
2025-03-14T05:10:00.6334570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.6334763Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:00.6334874Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:10:00.6334948Z         
2025-03-14T05:10:00.6335278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.6335440Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:00.6335506Z         
2025-03-14T05:10:00.6335851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.6336006Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:00.6336081Z         
2025-03-14T05:10:00.6336423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.6336564Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.6336692Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:10:00.6336858Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:00.6336996Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:10:00.6337064Z         
2025-03-14T05:10:00.6337419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.6337557Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.6337690Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:10:00.6337843Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:00.6338049Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:10:00.6338129Z         
2025-03-14T05:10:00.6338478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.6338630Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:00.6338803Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:00.6338941Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:10:00.6339016Z         
2025-03-14T05:10:00.6339344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.6339465Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:00.6339632Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:00.6339776Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:10:00.6339842Z         
2025-03-14T05:10:00.6340160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.6340262Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:00.6340389Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:00.6340454Z         
2025-03-14T05:10:00.6340765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.6340862Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:00.6340987Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:00.6341072Z         
2025-03-14T05:10:00.6341377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.6341505Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:00.6341655Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:00.6341731Z         
2025-03-14T05:10:00.6342034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.6342163Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:00.6342295Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:00.6342370Z         
2025-03-14T05:10:00.6342718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.6342920Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:00.6342986Z         
2025-03-14T05:10:00.6343328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.6343495Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:00.6343571Z         
2025-03-14T05:10:00.6343948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.6344196Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:00.6344277Z         
2025-03-14T05:10:00.6344728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.6344944Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:10:00.6345019Z         
2025-03-14T05:10:00.6345453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.6345613Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:10:00.6345764Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:00.6345915Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:00.6345981Z         
2025-03-14T05:10:00.6346365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.6346533Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:10:00.6346608Z         
2025-03-14T05:10:00.6346914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.6347070Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:00.6347134Z         
2025-03-14T05:10:00.6347449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.6347622Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:00.6347751Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6347960Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:10:00.6348025Z         
2025-03-14T05:10:00.6348352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.6348474Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:00.6348604Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:00.6348752Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:10:00.6348832Z         
2025-03-14T05:10:00.6349138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.6349268Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.6349360Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:00.6349499Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:10:00.6349565Z         
2025-03-14T05:10:00.6349880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.6350027Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:00.6350126Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:00.6350254Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:10:00.6350332Z         
2025-03-14T05:10:00.6350636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.6350830Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.6350948Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:10:00.6351022Z         
2025-03-14T05:10:00.6351320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.6351477Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.6351589Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:10:00.6351671Z         
2025-03-14T05:10:00.6351963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.6352118Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.6352229Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:10:00.6352296Z         
2025-03-14T05:10:00.6352602Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.6352793Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:00.6352902Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:10:00.6352967Z         
2025-03-14T05:10:00.6353304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.6353458Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:00.6353534Z         
2025-03-14T05:10:00.6353867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.6354038Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:00.6354103Z         
2025-03-14T05:10:00.6354445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.6354578Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.6354707Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:10:00.6354857Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:00.6355004Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:10:00.6355070Z         
2025-03-14T05:10:00.6355417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.6355553Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.6355684Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:10:00.6355837Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:00.6355981Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:10:00.6356050Z         
2025-03-14T05:10:00.6356386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.6356532Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:00.6356703Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:00.6356839Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:10:00.6356914Z         
2025-03-14T05:10:00.6357241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.6357362Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:00.6357527Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:00.6357658Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:10:00.6357733Z         
2025-03-14T05:10:00.6358041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.6358150Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:00.6358264Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:00.6358349Z         
2025-03-14T05:10:00.6358647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.6358756Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:00.6358871Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:00.6358961Z         
2025-03-14T05:10:00.6359253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.6359378Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:00.6359526Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:00.6359597Z         
2025-03-14T05:10:00.6359892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.6360013Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:00.6360139Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:00.6360212Z         
2025-03-14T05:10:00.6360547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.6360743Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:00.6360811Z         
2025-03-14T05:10:00.6361140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.6361295Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:00.6361366Z         
2025-03-14T05:10:00.6361734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.6361904Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:00.6361978Z         
2025-03-14T05:10:00.6362489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:00.6362625Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:00.6362698Z         
2025-03-14T05:10:00.6362984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6363137Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:10:00.6363200Z         
2025-03-14T05:10:00.6363620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.6363741Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:10:00.6363841Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:00.6363967Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:00.6364033Z         
2025-03-14T05:10:00.6364489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.6364620Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.6364852Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:10:00.6364938Z         
2025-03-14T05:10:00.6365404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.6365571Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.6365667Z         
2025-03-14T05:10:00.6367642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6367821Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:00.6367893Z         
2025-03-14T05:10:00.6368335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.6368471Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:10:00.6368589Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:00.6368706Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:00.6368788Z         
2025-03-14T05:10:00.6369288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.6369435Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.6369681Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:10:00.6369755Z         
2025-03-14T05:10:00.6370204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.6370462Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.6370531Z         
2025-03-14T05:10:00.6370829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6370972Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:00.6371041Z         
2025-03-14T05:10:00.6371485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.6371605Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:10:00.6371726Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:00.6371849Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:00.6371929Z         
2025-03-14T05:10:00.6372389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.6372539Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.6372776Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:10:00.6372859Z         
2025-03-14T05:10:00.6373320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.6373518Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.6373583Z         
2025-03-14T05:10:00.6373887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6374030Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:00.6374106Z         
2025-03-14T05:10:00.6374537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.6374663Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:10:00.6374772Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:00.6374899Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:00.6374968Z         
2025-03-14T05:10:00.6375435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.6375575Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.6375818Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:10:00.6375887Z         
2025-03-14T05:10:00.6376350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.6376516Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.6376595Z         
2025-03-14T05:10:00.6376925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6377061Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:00.6377129Z         
2025-03-14T05:10:00.6377564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.6377681Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:10:00.6377799Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:00.6377918Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:00.6378025Z         
2025-03-14T05:10:00.6378499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.6380592Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:00.6380881Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:10:00.6380952Z         
2025-03-14T05:10:00.6381641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.6381928Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.6382006Z         
2025-03-14T05:10:00.6382308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.6382478Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:00.6382547Z         
2025-03-14T05:10:00.6382835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.6383210Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:00.6383284Z         
2025-03-14T05:10:00.6383563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.6384031Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:00.6384100Z         
2025-03-14T05:10:00.6384479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.6384689Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:10:00.6384765Z         
2025-03-14T05:10:00.6385158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:00.6385323Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:00.6385389Z         
2025-03-14T05:10:00.6385758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.6385914Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:10:00.6385990Z         
2025-03-14T05:10:00.6386370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:00.6386516Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:00.6386584Z         
2025-03-14T05:10:00.6387077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:00.6387216Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:10:00.6387351Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:00.6387513Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:00.6387657Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:00.6387723Z         
2025-03-14T05:10:00.6388100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:00.6388220Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:00.6388311Z         
2025-03-14T05:10:00.6394070Z 
2025-03-14T05:10:00.6394181Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:00.6522627Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: 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1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:10:00.6523641Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:10:00.6524008Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6524432Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6524838Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6525207Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6525920Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6526283Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6526700Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6527100Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6527488Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6527869Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6528495Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6528909Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6529303Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6529692Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6530060Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6530409Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6530814Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6531207Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6531611Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6531993Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6532364Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.6532777Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6533196Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6533596Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.6533976Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.6534332Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6534762Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6535165Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6535547Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6535911Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6536257Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6536651Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6537053Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6537422Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6537811Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6538574Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6538999Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6539398Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6539772Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6540148Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6540487Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6540888Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6541285Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6541685Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6542065Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6542403Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6542809Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6543203Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6543588Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6543962Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6544375Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6544843Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6545318Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6545769Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6546196Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6546603Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6547074Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6547522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6547965Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6548389Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6548821Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6549278Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6549746Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6550188Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6551435Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6551845Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6552298Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6552781Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6553224Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6553677Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6554097Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.6554569Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6555045Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6555495Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.6555940Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.6556337Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6558006Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6558447Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6558865Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6559283Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6559650Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6560071Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6560499Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6560895Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6561310Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6561672Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6562087Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6562493Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6562878Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6563263Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6563620Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6564035Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6564434Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6564858Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6565256Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6565613Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6566028Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6566431Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6566820Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6567200Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6567572Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6567985Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6568399Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6568787Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6569171Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6569529Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6569931Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6570336Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6570726Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6571133Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6571493Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6571902Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6572300Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6572679Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6573054Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6573401Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6573793Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6574807Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6575223Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6575598Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6575938Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6577598Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6578009Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6578641Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6579021Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6579364Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6579819Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6580210Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6580606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6581393Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6581967Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6582683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6583109Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6583506Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6583945Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6584396Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.6584820Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6586047Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6586535Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.6586932Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.6587283Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6587683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6588200Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6588594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6588986Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6589326Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6589735Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6590141Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6590517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6590893Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6591252Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6591672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6592083Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6592464Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6592843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6593184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6593592Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6593985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6594368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6594763Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6595110Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6595513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6595901Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6596285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6596649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6596998Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6597392Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6597807Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6598205Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6598572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6598917Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6599316Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6599718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6600097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6600463Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6600808Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6601241Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6601639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6602011Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6602385Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6602732Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6603129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6603527Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6603925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6604313Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6604685Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6605087Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6605493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6605876Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6606256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6606615Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6607046Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6607473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6607867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6608256Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6608615Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6609040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6609440Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6609832Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6610201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6610572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6611003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6611400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6611788Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6612165Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6612521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6612925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6613327Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6613718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6614118Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6614475Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6614881Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6615290Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6615673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6616051Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6616400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6616804Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6617224Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6617620Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6618006Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6618803Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6619223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6619618Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6619989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6620360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6620735Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6621140Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6621532Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6621911Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6622285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6622630Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6623029Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6623423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6623834Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6624290Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6624670Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6625081Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6625479Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6625870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6626596Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6626958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6627355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6627805Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6628210Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6628573Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6628919Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6629319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6629719Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6630101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6630470Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6630840Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6631253Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6631652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6632023Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6632400Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6632749Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6633145Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6633543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6633913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6634310Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6634649Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6635058Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6635481Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6635856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6636228Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6636567Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6636987Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6637381Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6637780Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6638153Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6638496Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6638903Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6639293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6639672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6640033Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6640419Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6640827Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6641219Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6641603Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6641974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6642326Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6642722Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6643120Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6643518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6643902Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6644252Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6644650Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6645046Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6645424Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6645801Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6646148Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6646545Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6646971Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6647343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6647714Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6648054Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6648459Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6682672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6683843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6684401Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6684777Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6685232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6685641Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6686029Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6686403Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6686764Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6687166Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6687571Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6688010Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6688391Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6688740Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6689827Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6690250Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6690630Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6691005Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6691791Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6692236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6692655Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6693037Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6693414Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6693758Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6694166Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6694573Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6694947Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6695321Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6695692Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6696095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6696489Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6696871Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6698582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6699000Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6699423Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6699856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6700246Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6700629Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6700980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6701387Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6701797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6702194Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6702569Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6702933Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6703420Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6703838Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6704294Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6704680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6705049Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6705457Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6705871Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6706259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6706665Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6707038Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6707451Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6707863Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6708254Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6708650Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6709002Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6709454Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6709864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6710295Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6710684Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6711040Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6711464Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6711870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6712267Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6712654Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6713027Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6713450Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6713862Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6714262Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6714637Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6714997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6715411Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6715812Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6716201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6716607Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6716967Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6717373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6717778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6718173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6719041Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6719433Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6719841Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6720300Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6720706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6721068Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6721413Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6721810Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6722204Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6722574Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6722945Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6723291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6723717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6724117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6724488Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6724858Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6725198Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6725597Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6725991Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6726376Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6726768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6727870Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6728289Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6728683Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6729121Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6729501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6729838Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6730238Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6730708Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6731098Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6731474Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6731823Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6732236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6732632Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6733016Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6733385Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6733749Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6734169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6734563Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6734939Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6735307Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6735671Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6736072Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6736529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6736953Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6737321Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6737668Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6738119Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6738528Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6738911Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6739278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6739627Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6740042Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6740443Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6740831Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6741206Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6741557Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6741957Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6742366Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6742748Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6743135Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6743541Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6743961Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6744422Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6744818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6745222Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6745581Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6746010Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6746424Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6746846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6747247Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6747594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6748010Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6748417Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6748809Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6749185Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6749543Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6749989Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6750444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6750836Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6751214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6751572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6751980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6752389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6752778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6753171Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6753533Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6753958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6754364Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6754745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6755130Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6755487Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6755888Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6756295Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6756706Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6757090Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6757434Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6757843Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6758254Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6758637Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6759007Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6759342Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6759770Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6760176Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6760552Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6760927Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6761288Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.6761703Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6762106Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6762505Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.6762919Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.6763258Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6763655Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6764044Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6764421Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6764785Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6765129Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6765526Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6765943Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6766329Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6766725Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6767081Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6767490Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6767902Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6768288Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6768648Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6768990Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.6769423Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6769832Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6770219Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.6770607Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.6770981Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.6771379Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6771783Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6772157Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.6772554Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.6772907Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.6773310Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.6773708Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.6774078Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.6774459Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.6774690Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:10:00.6774925Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:10:00.6775140Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:10:00.6775357Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:10:00.6775601Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:10:00.6775819Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:10:00.6776034Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:10:00.6776246Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:10:00.6776458Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:10:00.6776670Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:10:00.6776888Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:10:00.6777092Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:10:00.6777310Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:10:00.6777514Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:10:00.6777733Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:10:00.6777933Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:10:00.6778305Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:10:00.6778654Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:10:00.6779020Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:10:00.6779361Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:10:00.6779707Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:10:00.6780038Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:10:00.6780351Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:10:00.6780726Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:10:00.6781081Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:10:00.6781646Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:10:00.6782092Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:10:00.6782179Z         
2025-03-14T05:10:00.6782492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6783049Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6783123Z         
2025-03-14T05:10:00.6783417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6787738Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6787909Z         
2025-03-14T05:10:00.6788229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:10:00.6788377Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:10:00.6788480Z         
2025-03-14T05:10:00.6788857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:10:00.6789114Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:10:00.6789191Z         
2025-03-14T05:10:00.6789453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6789955Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6790022Z         
2025-03-14T05:10:00.6790299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6792121Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6792199Z         
2025-03-14T05:10:00.6792499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6792645Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:10:00.6792721Z         
2025-03-14T05:10:00.6792982Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6793496Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6793566Z         
2025-03-14T05:10:00.6793845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6795661Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6795786Z         
2025-03-14T05:10:00.6796090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6796235Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:10:00.6796308Z         
2025-03-14T05:10:00.6796566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6797092Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6797157Z         
2025-03-14T05:10:00.6797422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6799189Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6799257Z         
2025-03-14T05:10:00.6799511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6799997Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.6800071Z         
2025-03-14T05:10:00.6800329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6802124Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6802239Z         
2025-03-14T05:10:00.6802514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6802665Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:10:00.6802729Z         
2025-03-14T05:10:00.6803014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6803162Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:10:00.6803233Z         
2025-03-14T05:10:00.6803477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6803957Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6804028Z         
2025-03-14T05:10:00.6806754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6808537Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6808645Z         
2025-03-14T05:10:00.6808937Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6809079Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:10:00.6809150Z         
2025-03-14T05:10:00.6809399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6809885Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6809949Z         
2025-03-14T05:10:00.6810210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6811955Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6812020Z         
2025-03-14T05:10:00.6812303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6812443Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:10:00.6812513Z         
2025-03-14T05:10:00.6812757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6813244Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6813402Z         
2025-03-14T05:10:00.6813660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6815429Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6815505Z         
2025-03-14T05:10:00.6815779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6815940Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:10:00.6816005Z         
2025-03-14T05:10:00.6816287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6816431Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:10:00.6816503Z         
2025-03-14T05:10:00.6816747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6817250Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6817314Z         
2025-03-14T05:10:00.6817578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6819424Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6819505Z         
2025-03-14T05:10:00.6819790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6819957Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:10:00.6820028Z         
2025-03-14T05:10:00.6820274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6820804Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6820871Z         
2025-03-14T05:10:00.6821150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6822932Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6823002Z         
2025-03-14T05:10:00.6823300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6823443Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:10:00.6823532Z         
2025-03-14T05:10:00.6823784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6824386Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6824458Z         
2025-03-14T05:10:00.6824736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6826512Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6826604Z         
2025-03-14T05:10:00.6826892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6827049Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:10:00.6827124Z         
2025-03-14T05:10:00.6827439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6827601Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:10:00.6827675Z         
2025-03-14T05:10:00.6827924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6829413Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6829501Z         
2025-03-14T05:10:00.6829792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6831583Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6831690Z         
2025-03-14T05:10:00.6831988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6832135Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:10:00.6832209Z         
2025-03-14T05:10:00.6832463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6832965Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6833033Z         
2025-03-14T05:10:00.6833305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6835086Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6835480Z         
2025-03-14T05:10:00.6835776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6835921Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:10:00.6835995Z         
2025-03-14T05:10:00.6836252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6836740Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6836805Z         
2025-03-14T05:10:00.6837071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6838812Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6838943Z         
2025-03-14T05:10:00.6839197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6839691Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.6839766Z         
2025-03-14T05:10:00.6840024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6841860Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6841952Z         
2025-03-14T05:10:00.6842260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6842421Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:10:00.6843806Z         
2025-03-14T05:10:00.6846576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6846773Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:10:00.6846853Z         
2025-03-14T05:10:00.6847117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6847620Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6847692Z         
2025-03-14T05:10:00.6847957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6849691Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6849832Z         
2025-03-14T05:10:00.6850110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6850258Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:10:00.6850326Z         
2025-03-14T05:10:00.6850576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6851057Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6851129Z         
2025-03-14T05:10:00.6851386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6853151Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6853247Z         
2025-03-14T05:10:00.6853526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6853677Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:10:00.6853741Z         
2025-03-14T05:10:00.6853990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6854472Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6854545Z         
2025-03-14T05:10:00.6854799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6856526Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6856621Z         
2025-03-14T05:10:00.6856899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6857067Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:10:00.6857131Z         
2025-03-14T05:10:00.6857422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6857571Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:10:00.6857643Z         
2025-03-14T05:10:00.6857892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6858396Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6858492Z         
2025-03-14T05:10:00.6858747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6860529Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6860603Z         
2025-03-14T05:10:00.6860892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6861041Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:10:00.6861106Z         
2025-03-14T05:10:00.6861366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6861859Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6861945Z         
2025-03-14T05:10:00.6862215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6863993Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6864072Z         
2025-03-14T05:10:00.6864464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6864627Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:10:00.6864697Z         
2025-03-14T05:10:00.6864998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6865517Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6865642Z         
2025-03-14T05:10:00.6865916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6867723Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6867798Z         
2025-03-14T05:10:00.6868075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6868231Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:10:00.6868295Z         
2025-03-14T05:10:00.6868584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6868749Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:10:00.6868823Z         
2025-03-14T05:10:00.6869078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6869573Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6869637Z         
2025-03-14T05:10:00.6869915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6871708Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6871794Z         
2025-03-14T05:10:00.6872092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6872233Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:10:00.6872334Z         
2025-03-14T05:10:00.6872582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6873080Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6873155Z         
2025-03-14T05:10:00.6873413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6875263Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6875354Z         
2025-03-14T05:10:00.6875637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6875786Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:10:00.6875850Z         
2025-03-14T05:10:00.6876104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6876599Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6876673Z         
2025-03-14T05:10:00.6876938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6878706Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6878798Z         
2025-03-14T05:10:00.6879120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6879281Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:10:00.6879345Z         
2025-03-14T05:10:00.6879634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6879782Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:10:00.6879855Z         
2025-03-14T05:10:00.6880101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6880587Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6880652Z         
2025-03-14T05:10:00.6880923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6882882Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6883010Z         
2025-03-14T05:10:00.6883293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6883428Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:10:00.6883502Z         
2025-03-14T05:10:00.6883743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6884219Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6884284Z         
2025-03-14T05:10:00.6884545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6886345Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6886446Z         
2025-03-14T05:10:00.6886736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6886872Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:10:00.6886948Z         
2025-03-14T05:10:00.6887191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6887670Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6887745Z         
2025-03-14T05:10:00.6887998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6889714Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6889809Z         
2025-03-14T05:10:00.6890054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6890544Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.6890609Z         
2025-03-14T05:10:00.6890873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6892679Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6892771Z         
2025-03-14T05:10:00.6893056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6893196Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:10:00.6893268Z         
2025-03-14T05:10:00.6893543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6893690Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:10:00.6893753Z         
2025-03-14T05:10:00.6894002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6894468Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6894540Z         
2025-03-14T05:10:00.6894795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6896545Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6897636Z         
2025-03-14T05:10:00.6898029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6898191Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:10:00.6898266Z         
2025-03-14T05:10:00.6898522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6898998Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6899105Z         
2025-03-14T05:10:00.6899364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6901169Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6901244Z         
2025-03-14T05:10:00.6901527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6901673Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:10:00.6901737Z         
2025-03-14T05:10:00.6901994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6902481Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6902556Z         
2025-03-14T05:10:00.6902848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6908283Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6908386Z         
2025-03-14T05:10:00.6908693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6908847Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:10:00.6908918Z         
2025-03-14T05:10:00.6909207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6909358Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:10:00.6909476Z         
2025-03-14T05:10:00.6909737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6910244Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6910320Z         
2025-03-14T05:10:00.6910585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6912361Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6912438Z         
2025-03-14T05:10:00.6912718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6912859Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:10:00.6912924Z         
2025-03-14T05:10:00.6913198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6913678Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6913761Z         
2025-03-14T05:10:00.6914013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6915710Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6915783Z         
2025-03-14T05:10:00.6916060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6916214Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:10:00.6916279Z         
2025-03-14T05:10:00.6916525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6917024Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6917099Z         
2025-03-14T05:10:00.6917352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6919052Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6919127Z         
2025-03-14T05:10:00.6919399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6919569Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:10:00.6919631Z         
2025-03-14T05:10:00.6919912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6920051Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:10:00.6920122Z         
2025-03-14T05:10:00.6920362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6920834Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6920907Z         
2025-03-14T05:10:00.6921164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6922886Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6922989Z         
2025-03-14T05:10:00.6923295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6923434Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:10:00.6923498Z         
2025-03-14T05:10:00.6923747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6924209Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6924281Z         
2025-03-14T05:10:00.6924536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6926260Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6926353Z         
2025-03-14T05:10:00.6926628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6926764Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:10:00.6926827Z         
2025-03-14T05:10:00.6927078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6927547Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6927620Z         
2025-03-14T05:10:00.6927880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6930706Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6930865Z         
2025-03-14T05:10:00.6931166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6931322Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:10:00.6931387Z         
2025-03-14T05:10:00.6931675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6931814Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:10:00.6931890Z         
2025-03-14T05:10:00.6932137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6932635Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6932702Z         
2025-03-14T05:10:00.6932977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6934767Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6934858Z         
2025-03-14T05:10:00.6935152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6935288Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:10:00.6935362Z         
2025-03-14T05:10:00.6935615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6936111Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6936184Z         
2025-03-14T05:10:00.6936446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6938258Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6938334Z         
2025-03-14T05:10:00.6938621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6938764Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:10:00.6938827Z         
2025-03-14T05:10:00.6939086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6939572Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6939646Z         
2025-03-14T05:10:00.6939908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6941772Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6941845Z         
2025-03-14T05:10:00.6942123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6942277Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:10:00.6942343Z         
2025-03-14T05:10:00.6942628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6942766Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:10:00.6942840Z         
2025-03-14T05:10:00.6943087Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6943589Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6943655Z         
2025-03-14T05:10:00.6943942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6945814Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6945886Z         
2025-03-14T05:10:00.6946178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6946312Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:10:00.6946384Z         
2025-03-14T05:10:00.6946635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6947121Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6947220Z         
2025-03-14T05:10:00.6947490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6949272Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6949340Z         
2025-03-14T05:10:00.6949628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6949757Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:10:00.6949848Z         
2025-03-14T05:10:00.6950104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6950647Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6950721Z         
2025-03-14T05:10:00.6950974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6952686Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6952752Z         
2025-03-14T05:10:00.6953030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6953180Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:10:00.6953243Z         
2025-03-14T05:10:00.6953541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6953679Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:10:00.6953754Z         
2025-03-14T05:10:00.6953998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6954467Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6954531Z         
2025-03-14T05:10:00.6954794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6956502Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6956594Z         
2025-03-14T05:10:00.6956877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6957035Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:10:00.6957105Z         
2025-03-14T05:10:00.6957345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6957823Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6957888Z         
2025-03-14T05:10:00.6958151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6959862Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6959941Z         
2025-03-14T05:10:00.6960230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6960358Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:10:00.6960428Z         
2025-03-14T05:10:00.6960673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6961150Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6961215Z         
2025-03-14T05:10:00.6961477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6963203Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6963284Z         
2025-03-14T05:10:00.6963594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6963736Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:10:00.6963808Z         
2025-03-14T05:10:00.6964079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6964226Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:10:00.6964290Z         
2025-03-14T05:10:00.6964539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6965003Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6965073Z         
2025-03-14T05:10:00.6965336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6967060Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6967152Z         
2025-03-14T05:10:00.6967439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6967570Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:10:00.6967644Z         
2025-03-14T05:10:00.6967885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6968360Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6968424Z         
2025-03-14T05:10:00.6968687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6970430Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6970525Z         
2025-03-14T05:10:00.6970809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6970941Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:10:00.6971014Z         
2025-03-14T05:10:00.6971255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6971736Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6971800Z         
2025-03-14T05:10:00.6972060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6973790Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6973874Z         
2025-03-14T05:10:00.6974153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6974297Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:10:00.6974369Z         
2025-03-14T05:10:00.6974643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6974789Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:10:00.6974851Z         
2025-03-14T05:10:00.6975099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6975565Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6975655Z         
2025-03-14T05:10:00.6975914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6977664Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6977740Z         
2025-03-14T05:10:00.6978019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6978165Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:10:00.6978228Z         
2025-03-14T05:10:00.6978476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6978947Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6979035Z         
2025-03-14T05:10:00.6979301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6981028Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6981102Z         
2025-03-14T05:10:00.6981381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6981791Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:10:00.6981870Z         
2025-03-14T05:10:00.6982117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6982681Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6982747Z         
2025-03-14T05:10:00.6983062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6984881Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6984965Z         
2025-03-14T05:10:00.6985256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6985411Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:10:00.6985488Z         
2025-03-14T05:10:00.6985775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6985956Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:10:00.6986022Z         
2025-03-14T05:10:00.6986282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6986758Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6986833Z         
2025-03-14T05:10:00.6987095Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6988877Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6988968Z         
2025-03-14T05:10:00.6989260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6989416Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:10:00.6989484Z         
2025-03-14T05:10:00.6989788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6990273Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.6990351Z         
2025-03-14T05:10:00.6990640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6992417Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6992510Z         
2025-03-14T05:10:00.6992794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6992943Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:10:00.6993009Z         
2025-03-14T05:10:00.6993268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6993758Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.6993832Z         
2025-03-14T05:10:00.6994102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6995879Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.6995971Z         
2025-03-14T05:10:00.6996256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.6996453Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:10:00.6996528Z         
2025-03-14T05:10:00.6996812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.6996963Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:10:00.6997030Z         
2025-03-14T05:10:00.6997286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.6997777Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.6997854Z         
2025-03-14T05:10:00.6998154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.6999961Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7000057Z         
2025-03-14T05:10:00.7000343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7000488Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:10:00.7000553Z         
2025-03-14T05:10:00.7000811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7001304Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7001377Z         
2025-03-14T05:10:00.7001643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7003582Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7003675Z         
2025-03-14T05:10:00.7003960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7004107Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:10:00.7004176Z         
2025-03-14T05:10:00.7004430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7004916Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7004991Z         
2025-03-14T05:10:00.7005252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7007049Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7007143Z         
2025-03-14T05:10:00.7007426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7007589Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:10:00.7007657Z         
2025-03-14T05:10:00.7007949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7008092Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:10:00.7008164Z         
2025-03-14T05:10:00.7008415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7008905Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7008995Z         
2025-03-14T05:10:00.7009256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7012349Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7012475Z         
2025-03-14T05:10:00.7012784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7012931Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:10:00.7012997Z         
2025-03-14T05:10:00.7013258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7013747Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7013845Z         
2025-03-14T05:10:00.7014110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7015908Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7015989Z         
2025-03-14T05:10:00.7016279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7016427Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:10:00.7016491Z         
2025-03-14T05:10:00.7016749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7017252Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7017328Z         
2025-03-14T05:10:00.7017622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7019414Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7019491Z         
2025-03-14T05:10:00.7019771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7019928Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:10:00.7019991Z         
2025-03-14T05:10:00.7020280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7020439Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:10:00.7020512Z         
2025-03-14T05:10:00.7020759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7021248Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7021313Z         
2025-03-14T05:10:00.7021579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7024747Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7024888Z         
2025-03-14T05:10:00.7025225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7025379Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:10:00.7025458Z         
2025-03-14T05:10:00.7025755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7026258Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7026330Z         
2025-03-14T05:10:00.7026594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7028392Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7028468Z         
2025-03-14T05:10:00.7028777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7028923Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:10:00.7028990Z         
2025-03-14T05:10:00.7029247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7029735Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7029810Z         
2025-03-14T05:10:00.7030073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7032818Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7032950Z         
2025-03-14T05:10:00.7033587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7033810Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:10:00.7033881Z         
2025-03-14T05:10:00.7034180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7034325Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:10:00.7034443Z         
2025-03-14T05:10:00.7034706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7035195Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7035261Z         
2025-03-14T05:10:00.7035537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7037322Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7037408Z         
2025-03-14T05:10:00.7037695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7037830Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:10:00.7037901Z         
2025-03-14T05:10:00.7038143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7038625Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7038691Z         
2025-03-14T05:10:00.7038957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7040722Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7040814Z         
2025-03-14T05:10:00.7041101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7041243Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:10:00.7041305Z         
2025-03-14T05:10:00.7041549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7042032Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7042101Z         
2025-03-14T05:10:00.7042355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7044079Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7044166Z         
2025-03-14T05:10:00.7044440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7044595Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:10:00.7044659Z         
2025-03-14T05:10:00.7044941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7045082Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:10:00.7045155Z         
2025-03-14T05:10:00.7045396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7045872Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7045951Z         
2025-03-14T05:10:00.7046218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7047972Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7048042Z         
2025-03-14T05:10:00.7048336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7048473Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:10:00.7048543Z         
2025-03-14T05:10:00.7048792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7050679Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7050817Z         
2025-03-14T05:10:00.7051111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7052888Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7052955Z         
2025-03-14T05:10:00.7053246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7053380Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:10:00.7053453Z         
2025-03-14T05:10:00.7053699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7054198Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7054276Z         
2025-03-14T05:10:00.7054564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7056313Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7056381Z         
2025-03-14T05:10:00.7056659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7056815Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:10:00.7056880Z         
2025-03-14T05:10:00.7057167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7057309Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:10:00.7057397Z         
2025-03-14T05:10:00.7057642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7058148Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7058214Z         
2025-03-14T05:10:00.7058481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7060254Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7060346Z         
2025-03-14T05:10:00.7060640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7060779Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:10:00.7060849Z         
2025-03-14T05:10:00.7061169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7061660Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7061724Z         
2025-03-14T05:10:00.7061991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7063799Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7063867Z         
2025-03-14T05:10:00.7064253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7064404Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:10:00.7064481Z         
2025-03-14T05:10:00.7064749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7065274Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7065346Z         
2025-03-14T05:10:00.7065631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7067453Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7067543Z         
2025-03-14T05:10:00.7067826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7068004Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:10:00.7068081Z         
2025-03-14T05:10:00.7068362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7068511Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:10:00.7068576Z         
2025-03-14T05:10:00.7068831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7069318Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7069383Z         
2025-03-14T05:10:00.7069652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7071449Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7071546Z         
2025-03-14T05:10:00.7071836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7071971Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:10:00.7072042Z         
2025-03-14T05:10:00.7072287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7072777Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7072843Z         
2025-03-14T05:10:00.7073108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7074943Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7075044Z         
2025-03-14T05:10:00.7075333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7075471Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:10:00.7075543Z         
2025-03-14T05:10:00.7075790Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7076286Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7076350Z         
2025-03-14T05:10:00.7076611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7078383Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7078477Z         
2025-03-14T05:10:00.7078754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7078898Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:10:00.7078969Z         
2025-03-14T05:10:00.7079240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7079388Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:10:00.7079450Z         
2025-03-14T05:10:00.7079699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7080160Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7080248Z         
2025-03-14T05:10:00.7080503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7082523Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7082606Z         
2025-03-14T05:10:00.7082884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7083025Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:10:00.7083090Z         
2025-03-14T05:10:00.7083340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7083820Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7083909Z         
2025-03-14T05:10:00.7084176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7085905Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7085982Z         
2025-03-14T05:10:00.7086273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7086404Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:10:00.7086474Z         
2025-03-14T05:10:00.7086717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7087224Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7087289Z         
2025-03-14T05:10:00.7087581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7090323Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7090457Z         
2025-03-14T05:10:00.7090781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7090939Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:10:00.7091014Z         
2025-03-14T05:10:00.7091300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7091452Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:10:00.7091538Z         
2025-03-14T05:10:00.7091795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7092278Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7092352Z         
2025-03-14T05:10:00.7092611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7094382Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7094477Z         
2025-03-14T05:10:00.7094760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7094913Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:10:00.7094978Z         
2025-03-14T05:10:00.7095267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7095758Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7095832Z         
2025-03-14T05:10:00.7096099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7097902Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7097978Z         
2025-03-14T05:10:00.7098280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7098446Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:10:00.7098510Z         
2025-03-14T05:10:00.7098765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7099266Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7099331Z         
2025-03-14T05:10:00.7099604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7101385Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7101474Z         
2025-03-14T05:10:00.7101761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7101968Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:10:00.7102040Z         
2025-03-14T05:10:00.7102319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7102476Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:10:00.7102540Z         
2025-03-14T05:10:00.7102793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7103280Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7103354Z         
2025-03-14T05:10:00.7103615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7105513Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7105640Z         
2025-03-14T05:10:00.7105924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7106070Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:10:00.7106135Z         
2025-03-14T05:10:00.7106389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7106884Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7106957Z         
2025-03-14T05:10:00.7107219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7109026Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7109118Z         
2025-03-14T05:10:00.7109406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7109552Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:10:00.7109617Z         
2025-03-14T05:10:00.7109883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7110371Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7110442Z         
2025-03-14T05:10:00.7110702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7112430Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7112519Z         
2025-03-14T05:10:00.7112791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7112958Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:10:00.7113030Z         
2025-03-14T05:10:00.7113304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7113456Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:10:00.7113521Z         
2025-03-14T05:10:00.7113770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7114240Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7114325Z         
2025-03-14T05:10:00.7114579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7116364Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7116441Z         
2025-03-14T05:10:00.7116718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7116859Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:10:00.7116947Z         
2025-03-14T05:10:00.7117197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7117673Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7117761Z         
2025-03-14T05:10:00.7118016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7119764Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7119836Z         
2025-03-14T05:10:00.7120113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7120252Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:10:00.7120315Z         
2025-03-14T05:10:00.7120564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7121065Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7121138Z         
2025-03-14T05:10:00.7121424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7123167Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7123242Z         
2025-03-14T05:10:00.7123513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7123677Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:10:00.7123743Z         
2025-03-14T05:10:00.7124027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7124169Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:10:00.7124256Z         
2025-03-14T05:10:00.7124498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7126143Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7126251Z         
2025-03-14T05:10:00.7126530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7128275Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7128416Z         
2025-03-14T05:10:00.7128702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7128848Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:10:00.7128914Z         
2025-03-14T05:10:00.7129219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7129718Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7129790Z         
2025-03-14T05:10:00.7130062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7132394Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7132477Z         
2025-03-14T05:10:00.7132800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7132949Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:10:00.7133016Z         
2025-03-14T05:10:00.7133284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7133787Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7133864Z         
2025-03-14T05:10:00.7134136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7135929Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7136022Z         
2025-03-14T05:10:00.7136307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7136507Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:10:00.7136575Z         
2025-03-14T05:10:00.7136872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7137018Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:10:00.7137094Z         
2025-03-14T05:10:00.7137351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7137847Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7137915Z         
2025-03-14T05:10:00.7138227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7140041Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7140128Z         
2025-03-14T05:10:00.7140422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7140560Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:10:00.7140635Z         
2025-03-14T05:10:00.7140886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7141388Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7141461Z         
2025-03-14T05:10:00.7141722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7143533Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7143626Z         
2025-03-14T05:10:00.7143911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7144056Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:10:00.7144180Z         
2025-03-14T05:10:00.7144458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7144963Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7145035Z         
2025-03-14T05:10:00.7145314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7147189Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7147284Z         
2025-03-14T05:10:00.7147567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7147735Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:10:00.7147801Z         
2025-03-14T05:10:00.7148093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7148239Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:10:00.7148312Z         
2025-03-14T05:10:00.7148561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7149050Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7149147Z         
2025-03-14T05:10:00.7149423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7152467Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7152567Z         
2025-03-14T05:10:00.7152882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7153026Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T05:10:00.7153105Z         
2025-03-14T05:10:00.7153357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7153862Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7153969Z         
2025-03-14T05:10:00.7154237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7156008Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7156075Z         
2025-03-14T05:10:00.7156365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7156500Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T05:10:00.7156571Z         
2025-03-14T05:10:00.7156814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7157343Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7157417Z         
2025-03-14T05:10:00.7157711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7159452Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7159527Z         
2025-03-14T05:10:00.7159770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7160257Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.7160323Z         
2025-03-14T05:10:00.7160586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7162388Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7162463Z         
2025-03-14T05:10:00.7162747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7162893Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T05:10:00.7162965Z         
2025-03-14T05:10:00.7163237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7163386Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T05:10:00.7163468Z         
2025-03-14T05:10:00.7163718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7164216Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7164291Z         
2025-03-14T05:10:00.7164551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7166349Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7166429Z         
2025-03-14T05:10:00.7166706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7166847Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T05:10:00.7166909Z         
2025-03-14T05:10:00.7167162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7167654Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7167725Z         
2025-03-14T05:10:00.7167980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7169712Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7169788Z         
2025-03-14T05:10:00.7170063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7170259Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T05:10:00.7170324Z         
2025-03-14T05:10:00.7170574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7171098Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7171168Z         
2025-03-14T05:10:00.7171436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7173201Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7173278Z         
2025-03-14T05:10:00.7173565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7173742Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T05:10:00.7173816Z         
2025-03-14T05:10:00.7174912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7175110Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T05:10:00.7175181Z         
2025-03-14T05:10:00.7175453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7175933Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7176012Z         
2025-03-14T05:10:00.7176276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7178061Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7178172Z         
2025-03-14T05:10:00.7178503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7178648Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T05:10:00.7178713Z         
2025-03-14T05:10:00.7178967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7179448Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7179524Z         
2025-03-14T05:10:00.7179786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7181795Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7181951Z         
2025-03-14T05:10:00.7182242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7182387Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T05:10:00.7182455Z         
2025-03-14T05:10:00.7182713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7183210Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7183286Z         
2025-03-14T05:10:00.7183556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7185570Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7185679Z         
2025-03-14T05:10:00.7185965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7186140Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T05:10:00.7186205Z         
2025-03-14T05:10:00.7186499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7186656Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T05:10:00.7186721Z         
2025-03-14T05:10:00.7186981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7187554Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.7187629Z         
2025-03-14T05:10:00.7187879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7188436Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.7188519Z         
2025-03-14T05:10:00.7188942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.7189212Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T05:10:00.7189289Z         
2025-03-14T05:10:00.7189537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7190113Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.7190192Z         
2025-03-14T05:10:00.7190826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.7191097Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T05:10:00.7191167Z         
2025-03-14T05:10:00.7191435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7192025Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.7192103Z         
2025-03-14T05:10:00.7192576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.7192908Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:10:00.7192975Z         
2025-03-14T05:10:00.7193234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7193810Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.7193885Z         
2025-03-14T05:10:00.7194234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.7194443Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T05:10:00.7194513Z         
2025-03-14T05:10:00.7194758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7195334Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.7195417Z         
2025-03-14T05:10:00.7195823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.7196146Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:10:00.7196224Z         
2025-03-14T05:10:00.7196481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7197054Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.7197119Z         
2025-03-14T05:10:00.7197458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.7197669Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T05:10:00.7197749Z         
2025-03-14T05:10:00.7197998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7198621Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.7198696Z         
2025-03-14T05:10:00.7199050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:10:00.7199266Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:10:00.7199334Z         
2025-03-14T05:10:00.7199766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7199919Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.7199993Z         
2025-03-14T05:10:00.7200284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7200432Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:00.7200496Z         
2025-03-14T05:10:00.7200927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7203281Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.7203378Z         
2025-03-14T05:10:00.7203675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7203826Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:10:00.7203892Z         
2025-03-14T05:10:00.7204273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.7204453Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:10:00.7204564Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:10:00.7204687Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:10:00.7204763Z         
2025-03-14T05:10:00.7205126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.7205254Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:10:00.7205326Z         
2025-03-14T05:10:00.7208059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.7208285Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:10:00.7208354Z         
2025-03-14T05:10:00.7209311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.7209600Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:10:00.7209677Z         
2025-03-14T05:10:00.7210097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.7210246Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:10:00.7210673Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:10:00.7210797Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:10:00.7210925Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:10:00.7210990Z         
2025-03-14T05:10:00.7211429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7211582Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.7211653Z         
2025-03-14T05:10:00.7211944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7212093Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:10:00.7212157Z         
2025-03-14T05:10:00.7212588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7212817Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.7212907Z         
2025-03-14T05:10:00.7213189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7213332Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:10:00.7213399Z         
2025-03-14T05:10:00.7213765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.7213958Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:10:00.7214071Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:10:00.7214196Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:10:00.7214273Z         
2025-03-14T05:10:00.7214591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.7214727Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:10:00.7214792Z         
2025-03-14T05:10:00.7215116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.7215239Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:10:00.7215311Z         
2025-03-14T05:10:00.7215676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.7215925Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:10:00.7215989Z         
2025-03-14T05:10:00.7216402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.7216554Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:10:00.7216961Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:10:00.7217089Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:10:00.7217206Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:10:00.7217278Z         
2025-03-14T05:10:00.7217693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7217848Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.7217917Z         
2025-03-14T05:10:00.7218219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7218358Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:10:00.7218431Z         
2025-03-14T05:10:00.7218857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7219041Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.7219122Z         
2025-03-14T05:10:00.7219426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7219564Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:10:00.7219637Z         
2025-03-14T05:10:00.7220014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.7220219Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:10:00.7220325Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:10:00.7220457Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:10:00.7220522Z         
2025-03-14T05:10:00.7220857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.7220982Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:10:00.7221060Z         
2025-03-14T05:10:00.7221384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.7221514Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:10:00.7221580Z         
2025-03-14T05:10:00.7221969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.7222196Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:10:00.7222270Z         
2025-03-14T05:10:00.7222707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.7222836Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:10:00.7223259Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:10:00.7223382Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:10:00.7223570Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:10:00.7223642Z         
2025-03-14T05:10:00.7224089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7224337Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.7224417Z         
2025-03-14T05:10:00.7224718Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7224864Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:10:00.7224931Z         
2025-03-14T05:10:00.7225406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7225564Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.7225637Z         
2025-03-14T05:10:00.7225958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7226094Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:10:00.7226167Z         
2025-03-14T05:10:00.7226536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.7226732Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:10:00.7226846Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:10:00.7226968Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:10:00.7227033Z         
2025-03-14T05:10:00.7227354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.7227477Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:10:00.7227552Z         
2025-03-14T05:10:00.7227870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.7227997Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:10:00.7228061Z         
2025-03-14T05:10:00.7228446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.7228674Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:10:00.7228748Z         
2025-03-14T05:10:00.7229172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.7229310Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:10:00.7229724Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:10:00.7229855Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:10:00.7229970Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:10:00.7230044Z         
2025-03-14T05:10:00.7230475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7230619Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.7230693Z         
2025-03-14T05:10:00.7230980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7231123Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:10:00.7231188Z         
2025-03-14T05:10:00.7231636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.7231804Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.7231878Z         
2025-03-14T05:10:00.7232164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7232308Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:10:00.7233896Z         
2025-03-14T05:10:00.7234348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.7234549Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:10:00.7234673Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:10:00.7234799Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:10:00.7234875Z         
2025-03-14T05:10:00.7235201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.7235336Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:10:00.7235403Z         
2025-03-14T05:10:00.7235733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.7235854Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:10:00.7235926Z         
2025-03-14T05:10:00.7236337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.7236554Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:10:00.7236620Z         
2025-03-14T05:10:00.7237057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.7237184Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:10:00.7237607Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:10:00.7237731Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:10:00.7237853Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:10:00.7237919Z         
2025-03-14T05:10:00.7238220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.7238362Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T05:10:00.7238492Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T05:10:00.7238623Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T05:10:00.7238746Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T05:10:00.7238873Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T05:10:00.7238940Z         
2025-03-14T05:10:00.7239225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7239754Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T05:10:00.7239832Z         
2025-03-14T05:10:00.7240110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.7240321Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T05:10:00.7240385Z         
2025-03-14T05:10:00.7240774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.7241292Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.7241367Z         
2025-03-14T05:10:00.7241725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.7242251Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T05:10:00.7242335Z         
2025-03-14T05:10:00.7242597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7243099Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T05:10:00.7243166Z         
2025-03-14T05:10:00.7243445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.7243638Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T05:10:00.7243710Z         
2025-03-14T05:10:00.7244082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.7244598Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.7244663Z         
2025-03-14T05:10:00.7245027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.7245518Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T05:10:00.7245591Z         
2025-03-14T05:10:00.7245850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7246352Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T05:10:00.7246421Z         
2025-03-14T05:10:00.7246685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.7246874Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T05:10:00.7246940Z         
2025-03-14T05:10:00.7247305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.7247792Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.7247862Z         
2025-03-14T05:10:00.7248588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.7249735Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T05:10:00.7249845Z         
2025-03-14T05:10:00.7250125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7250603Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T05:10:00.7250679Z         
2025-03-14T05:10:00.7250949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.7251137Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T05:10:00.7251202Z         
2025-03-14T05:10:00.7251575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.7252066Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.7252129Z         
2025-03-14T05:10:00.7252484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.7252963Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T05:10:00.7253055Z         
2025-03-14T05:10:00.7253318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7254060Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:10:00.7254125Z         
2025-03-14T05:10:00.7254397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.7254569Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T05:10:00.7254641Z         
2025-03-14T05:10:00.7255006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.7255826Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:10:00.7255901Z         
2025-03-14T05:10:00.7256268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.7257089Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:10:00.7257154Z         
2025-03-14T05:10:00.7257496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:10:00.7257671Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:10:00.7257822Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:10:00.7258028Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:10:00.7258184Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:10:00.7258352Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:10:00.7258492Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:10:00.7258644Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:10:00.7258779Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:10:00.7258932Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:10:00.7259097Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:10:00.7259186Z         
2025-03-14T05:10:00.7259618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:10:00.7259810Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T05:10:00.7260003Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:10:00.7260195Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:10:00.7260363Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T05:10:00.7260555Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:10:00.7260731Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:10:00.7260893Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T05:10:00.7261061Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:10:00.7261239Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:10:00.7261386Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T05:10:00.7261555Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:10:00.7261756Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:10:00.7261907Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T05:10:00.7262074Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:10:00.7262254Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:10:00.7262327Z         
2025-03-14T05:10:00.7262730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.7262942Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:10:00.7263010Z         
2025-03-14T05:10:00.7263459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.7263614Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:10:00.7263774Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:00.7263917Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:00.7263991Z         
2025-03-14T05:10:00.7264439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.7264630Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:00.7264700Z         
2025-03-14T05:10:00.7265051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.7265213Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:00.7265287Z         
2025-03-14T05:10:00.7265629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.7265769Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:00.7265897Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7266062Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:00.7266128Z         
2025-03-14T05:10:00.7266466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.7266596Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:00.7266729Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:00.7266887Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:10:00.7266960Z         
2025-03-14T05:10:00.7267273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.7267405Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7267498Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:00.7267634Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:10:00.7267720Z         
2025-03-14T05:10:00.7268040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.7268190Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:00.7268290Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:00.7268440Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:10:00.7268516Z         
2025-03-14T05:10:00.7268854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.7269021Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.7269137Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:10:00.7269210Z         
2025-03-14T05:10:00.7269517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.7269671Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.7269795Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:10:00.7269862Z         
2025-03-14T05:10:00.7270165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.7270314Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.7270436Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:10:00.7270502Z         
2025-03-14T05:10:00.7270810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.7271025Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:00.7271145Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:10:00.7271210Z         
2025-03-14T05:10:00.7271550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.7271691Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:00.7271761Z         
2025-03-14T05:10:00.7272089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.7272231Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:00.7272296Z         
2025-03-14T05:10:00.7272645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.7272782Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:00.7272920Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:10:00.7273072Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:00.7273222Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:10:00.7273286Z         
2025-03-14T05:10:00.7273639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.7273798Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:00.7273931Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:10:00.7274080Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:00.7274242Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:10:00.7274310Z         
2025-03-14T05:10:00.7274662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.7275725Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:00.7275912Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:00.7276053Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:10:00.7276133Z         
2025-03-14T05:10:00.7276475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.7276603Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:00.7276783Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:00.7276919Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:10:00.7276993Z         
2025-03-14T05:10:00.7277308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.7277419Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:00.7277537Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:00.7277681Z         
2025-03-14T05:10:00.7278008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.7278115Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:00.7278234Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:00.7278306Z         
2025-03-14T05:10:00.7278606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.7278730Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:00.7278861Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:00.7278936Z         
2025-03-14T05:10:00.7279243Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.7279365Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:00.7279490Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:00.7279561Z         
2025-03-14T05:10:00.7279900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.7280085Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:00.7280147Z         
2025-03-14T05:10:00.7280479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.7280657Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:00.7280729Z         
2025-03-14T05:10:00.7281091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.7281297Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:00.7281362Z         
2025-03-14T05:10:00.7282118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.7282329Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:00.7282406Z         
2025-03-14T05:10:00.7282826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.7282990Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:10:00.7283142Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:00.7283290Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:00.7283353Z         
2025-03-14T05:10:00.7283725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.7283891Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:00.7283966Z         
2025-03-14T05:10:00.7284341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.7284519Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:00.7284592Z         
2025-03-14T05:10:00.7284896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.7285035Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:00.7285163Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7285317Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:10:00.7285380Z         
2025-03-14T05:10:00.7285695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.7285818Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:00.7285947Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:00.7286098Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:10:00.7286170Z         
2025-03-14T05:10:00.7286467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.7286597Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7286691Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:00.7286830Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:10:00.7286920Z         
2025-03-14T05:10:00.7287229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.7287378Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:00.7287479Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:00.7287635Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:10:00.7287707Z         
2025-03-14T05:10:00.7288007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.7288166Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.7288279Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:10:00.7288351Z         
2025-03-14T05:10:00.7288642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.7288796Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.7288906Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:10:00.7288980Z         
2025-03-14T05:10:00.7289264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.7289415Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.7289522Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:10:00.7289597Z         
2025-03-14T05:10:00.7289903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.7290109Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:00.7290216Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:10:00.7290288Z         
2025-03-14T05:10:00.7290610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.7290754Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:00.7290818Z         
2025-03-14T05:10:00.7291144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.7291287Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:00.7291352Z         
2025-03-14T05:10:00.7291689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.7291824Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.7291957Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:10:00.7292110Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:00.7292258Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:10:00.7292324Z         
2025-03-14T05:10:00.7292666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.7292820Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.7292954Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:10:00.7293105Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:00.7293267Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:10:00.7293332Z         
2025-03-14T05:10:00.7293661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.7293774Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:00.7293941Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:00.7294079Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:10:00.7294152Z         
2025-03-14T05:10:00.7294467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.7294588Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:00.7294750Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:00.7294888Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:10:00.7294953Z         
2025-03-14T05:10:00.7295254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.7295354Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:00.7295494Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:00.7295572Z         
2025-03-14T05:10:00.7295898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.7295994Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:00.7296117Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:00.7296180Z         
2025-03-14T05:10:00.7296480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.7296594Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:00.7296737Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:00.7296802Z         
2025-03-14T05:10:00.7297099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.7297212Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:00.7297349Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:00.7297412Z         
2025-03-14T05:10:00.7297749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.7297936Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:00.7298008Z         
2025-03-14T05:10:00.7298351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.7298523Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:00.7298595Z         
2025-03-14T05:10:00.7298984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.7299168Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:00.7299233Z         
2025-03-14T05:10:00.7299627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.7299831Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:00.7299902Z         
2025-03-14T05:10:00.7300335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.7300497Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:10:00.7300647Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:00.7300793Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:00.7300860Z         
2025-03-14T05:10:00.7301238Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.7301424Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:10:00.7301514Z         
2025-03-14T05:10:00.7301828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.7301981Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:00.7302047Z         
2025-03-14T05:10:00.7303537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.7303680Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:00.7303817Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7303971Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:10:00.7304053Z         
2025-03-14T05:10:00.7304462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.7304608Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:00.7304733Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:00.7304899Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:10:00.7304967Z         
2025-03-14T05:10:00.7305301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.7305428Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7305565Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:00.7305698Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:10:00.7305775Z         
2025-03-14T05:10:00.7306083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.7306236Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:00.7306347Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:00.7306485Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:10:00.7306550Z         
2025-03-14T05:10:00.7306862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.7307020Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.7307138Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:10:00.7307214Z         
2025-03-14T05:10:00.7307512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.7307668Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.7307781Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:10:00.7307854Z         
2025-03-14T05:10:00.7308150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.7308308Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.7308419Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:10:00.7308493Z         
2025-03-14T05:10:00.7308810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.7309016Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:00.7309129Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:10:00.7309202Z         
2025-03-14T05:10:00.7309531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.7309675Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:00.7309739Z         
2025-03-14T05:10:00.7310074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.7310215Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:00.7310291Z         
2025-03-14T05:10:00.7310627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.7310773Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.7310897Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:10:00.7311056Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:00.7311196Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:10:00.7311286Z         
2025-03-14T05:10:00.7311633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.7311779Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.7311900Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:10:00.7312076Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:00.7312215Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:10:00.7312286Z         
2025-03-14T05:10:00.7312610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.7312734Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:00.7312905Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:00.7313042Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:10:00.7313115Z         
2025-03-14T05:10:00.7313444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.7313563Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:00.7313728Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:00.7313866Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:10:00.7313930Z         
2025-03-14T05:10:00.7314244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.7314376Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:00.7314527Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:00.7314593Z         
2025-03-14T05:10:00.7314910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.7315005Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:00.7315124Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:00.7315188Z         
2025-03-14T05:10:00.7315494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.7315609Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:00.7315753Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:00.7315819Z         
2025-03-14T05:10:00.7316126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.7316240Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:00.7316377Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:00.7316443Z         
2025-03-14T05:10:00.7316794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.7316980Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:00.7317071Z         
2025-03-14T05:10:00.7317400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.7317570Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:00.7317636Z         
2025-03-14T05:10:00.7318036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.7318211Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:00.7318283Z         
2025-03-14T05:10:00.7318678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.7318906Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:10:00.7318969Z         
2025-03-14T05:10:00.7319395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.7319542Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:10:00.7319695Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:00.7319836Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:00.7319898Z         
2025-03-14T05:10:00.7320261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.7320445Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:10:00.7320530Z         
2025-03-14T05:10:00.7320837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.7320984Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:00.7321047Z         
2025-03-14T05:10:00.7321361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.7321486Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:00.7321614Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7321761Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:10:00.7321833Z         
2025-03-14T05:10:00.7322147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.7322275Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:00.7322394Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:00.7322552Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:10:00.7322614Z         
2025-03-14T05:10:00.7322926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.7323046Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7323165Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:00.7323299Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:10:00.7323372Z         
2025-03-14T05:10:00.7323676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.7323847Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:00.7323944Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:00.7324083Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:10:00.7324147Z         
2025-03-14T05:10:00.7324462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.7324613Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.7324731Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:10:00.7324797Z         
2025-03-14T05:10:00.7325096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.7325246Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.7325365Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:10:00.7325428Z         
2025-03-14T05:10:00.7325737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.7325880Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.7325998Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:10:00.7326078Z         
2025-03-14T05:10:00.7326399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.7326585Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:00.7326694Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:10:00.7326763Z         
2025-03-14T05:10:00.7327084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.7327226Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:00.7327290Z         
2025-03-14T05:10:00.7327620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.7327754Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:00.7327825Z         
2025-03-14T05:10:00.7328159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.7328306Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.7329835Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:10:00.7330021Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:00.7330161Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:10:00.7330275Z         
2025-03-14T05:10:00.7330617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.7330759Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.7330898Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:10:00.7331056Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:00.7331191Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:10:00.7331263Z         
2025-03-14T05:10:00.7331584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.7331705Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:00.7331867Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:00.7332014Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:10:00.7332079Z         
2025-03-14T05:10:00.7332425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.7332535Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:00.7332705Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:00.7332832Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:10:00.7332906Z         
2025-03-14T05:10:00.7333227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.7333347Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:00.7333462Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:00.7333534Z         
2025-03-14T05:10:00.7334657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.7334767Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:00.7334884Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:00.7334957Z         
2025-03-14T05:10:00.7335253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.7335378Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:00.7335511Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:00.7335590Z         
2025-03-14T05:10:00.7335885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.7336008Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:00.7336135Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:00.7336207Z         
2025-03-14T05:10:00.7336540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.7336732Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:00.7336827Z         
2025-03-14T05:10:00.7337148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.7337316Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:00.7337379Z         
2025-03-14T05:10:00.7337776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.7337944Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:00.7338016Z         
2025-03-14T05:10:00.7338402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.7338615Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:10:00.7338680Z         
2025-03-14T05:10:00.7339111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.7339256Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:10:00.7339410Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:00.7339545Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:00.7339621Z         
2025-03-14T05:10:00.7340009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.7340204Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:10:00.7340271Z         
2025-03-14T05:10:00.7340589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.7340734Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:00.7340810Z         
2025-03-14T05:10:00.7341116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.7341251Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:00.7341377Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7341532Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:10:00.7341598Z         
2025-03-14T05:10:00.7341919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.7342043Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:00.7342170Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:00.7342319Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:10:00.7342391Z         
2025-03-14T05:10:00.7342694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.7342841Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.7342934Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:00.7343071Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:10:00.7343136Z         
2025-03-14T05:10:00.7343463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.7343610Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:00.7343709Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:00.7343842Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:10:00.7343908Z         
2025-03-14T05:10:00.7344302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.7344467Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.7344594Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:10:00.7344664Z         
2025-03-14T05:10:00.7344978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.7345141Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.7345263Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:10:00.7345328Z         
2025-03-14T05:10:00.7345633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.7345781Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.7345920Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:10:00.7346002Z         
2025-03-14T05:10:00.7346316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.7346500Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:00.7346616Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:10:00.7346681Z         
2025-03-14T05:10:00.7347028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.7347165Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:00.7347242Z         
2025-03-14T05:10:00.7347583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.7347727Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:00.7347793Z         
2025-03-14T05:10:00.7348145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.7348283Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.7348416Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:10:00.7348571Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:00.7348734Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:10:00.7348799Z         
2025-03-14T05:10:00.7350426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.7350627Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.7350844Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:10:00.7351016Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:00.7351155Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:10:00.7351227Z         
2025-03-14T05:10:00.7351569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.7351700Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:00.7351861Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:00.7352001Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:10:00.7352066Z         
2025-03-14T05:10:00.7352412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.7352528Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:00.7352700Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:00.7352831Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:10:00.7352907Z         
2025-03-14T05:10:00.7353255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.7353381Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:00.7353498Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:00.7353573Z         
2025-03-14T05:10:00.7353882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.7353987Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:00.7354320Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:00.7354421Z         
2025-03-14T05:10:00.7354803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.7354975Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:00.7355123Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:00.7355203Z         
2025-03-14T05:10:00.7355505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.7355628Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:00.7355755Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:00.7355830Z         
2025-03-14T05:10:00.7356175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.7356398Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:00.7356465Z         
2025-03-14T05:10:00.7356807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.7356966Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:00.7357057Z         
2025-03-14T05:10:00.7357441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.7357621Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:00.7357685Z         
2025-03-14T05:10:00.7358169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:00.7358306Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:00.7358380Z         
2025-03-14T05:10:00.7358673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7358826Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:10:00.7358892Z         
2025-03-14T05:10:00.7359343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.7359479Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:10:00.7359584Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:00.7359718Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:00.7359796Z         
2025-03-14T05:10:00.7360250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.7360384Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.7360615Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:10:00.7360682Z         
2025-03-14T05:10:00.7361131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.7361298Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.7361370Z         
2025-03-14T05:10:00.7361655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7361784Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:00.7361850Z         
2025-03-14T05:10:00.7362277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.7362393Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:10:00.7362509Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:00.7362649Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:00.7362721Z         
2025-03-14T05:10:00.7363175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.7363313Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.7363838Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:10:00.7363914Z         
2025-03-14T05:10:00.7364355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.7364527Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.7364591Z         
2025-03-14T05:10:00.7364887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7365011Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:00.7365083Z         
2025-03-14T05:10:00.7365502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.7365621Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:10:00.7365727Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:00.7365850Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:00.7365916Z         
2025-03-14T05:10:00.7366378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.7366529Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.7366758Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:10:00.7366827Z         
2025-03-14T05:10:00.7368221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.7368413Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.7368483Z         
2025-03-14T05:10:00.7368782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7368908Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:00.7368981Z         
2025-03-14T05:10:00.7369395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.7369516Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:10:00.7369621Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:00.7369742Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:00.7369806Z         
2025-03-14T05:10:00.7370249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.7370405Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.7370638Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:10:00.7370720Z         
2025-03-14T05:10:00.7371168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.7371328Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.7371402Z         
2025-03-14T05:10:00.7371689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7371820Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:00.7371885Z         
2025-03-14T05:10:00.7372306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.7372418Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:10:00.7372529Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:00.7372642Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:00.7372716Z         
2025-03-14T05:10:00.7373152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.7373339Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:00.7373587Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:10:00.7373653Z         
2025-03-14T05:10:00.7374096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.7374253Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.7374325Z         
2025-03-14T05:10:00.7374606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.7374735Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:00.7374801Z         
2025-03-14T05:10:00.7375080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.7375446Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:00.7375519Z         
2025-03-14T05:10:00.7375787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.7376238Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:00.7376320Z         
2025-03-14T05:10:00.7376592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.7376851Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:10:00.7376925Z         
2025-03-14T05:10:00.7377305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:00.7377451Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:00.7377515Z         
2025-03-14T05:10:00.7377811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.7377956Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:10:00.7378031Z         
2025-03-14T05:10:00.7378404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:00.7378538Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:00.7378608Z         
2025-03-14T05:10:00.7379075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:00.7379217Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:10:00.7379337Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:00.7379510Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:00.7379654Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:00.7379723Z         
2025-03-14T05:10:00.7380075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:00.7380195Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:00.7380260Z         
2025-03-14T05:10:00.7380272Z 
2025-03-14T05:10:00.7380370Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:00.7502461Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: 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1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:10:00.7503535Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:10:00.7503972Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7504500Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7505005Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7505436Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7505784Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7506139Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7506569Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7506987Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7507377Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7507745Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7508096Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7508498Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7508901Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7509282Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7509668Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7510033Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7510434Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7510838Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7511212Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7511587Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7511950Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.7512359Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7512773Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7513186Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.7513588Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.7513928Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7514331Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7514735Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7515108Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7515480Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7515821Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7516283Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7516695Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7517076Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7517450Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7517792Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7518196Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7518584Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7518962Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7519348Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7519692Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7520110Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7520499Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7520883Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7521247Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7521595Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7521989Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7522404Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7522799Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7523168Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7523510Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7523904Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7524302Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7524674Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7525047Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7525391Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7525799Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7526210Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7526580Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7526952Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7527299Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7527697Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7528093Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7528464Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7528850Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7530534Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7530979Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7531392Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7531778Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7532153Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7532516Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.7532941Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7533376Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7533781Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.7534182Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.7534522Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7534925Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7535314Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7536212Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7536582Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7536927Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7537360Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7537784Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7538167Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7538529Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7538878Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7539273Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7539670Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7540050Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7540432Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7540798Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7541194Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7541592Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7541968Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7542345Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7542689Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7543085Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7543498Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7543894Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7544362Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7544745Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7545198Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7545643Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7546060Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7546474Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7546859Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7547334Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7547795Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7548220Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7548637Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7549019Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7549470Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7549907Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7550332Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7550756Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7551158Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7551610Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7552041Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7552464Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7552830Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7553177Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7553572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7553984Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7554376Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7554740Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7555089Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7555484Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7555884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7556266Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7556630Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7556997Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7557405Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7557802Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7558169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7558539Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7558900Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.7559306Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7559717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7560103Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.7560508Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.7560860Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7561264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7561658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7562031Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7562404Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7562743Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7563140Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7563549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7563948Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7564318Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7564656Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7565060Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7565453Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7566521Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7566949Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7567344Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7568111Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7568568Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7568999Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7569380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7569744Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7570186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7570627Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7571078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7571525Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7571909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7572372Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7572815Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7573228Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7573639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7574002Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7574460Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7574916Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7575348Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7575759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7576148Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7576593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7577034Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7577444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7577845Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7578242Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7578720Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7579139Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7579536Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7579917Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7580263Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7580673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7581067Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7582237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7582632Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7583050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7583465Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7583869Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7584323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7584706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7585078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7585513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7585985Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7586399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7586779Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7587123Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7587519Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7587922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7588299Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7588663Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7589039Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7589456Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7589855Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7590227Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7590604Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7590955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7591352Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7591745Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7592131Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7592516Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7592862Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7593279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7593676Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7594050Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7594419Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7594753Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7595154Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7595563Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7595955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7596360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7599080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7599555Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7599955Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7600761Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7601142Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7601566Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7602038Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7602431Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7602812Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7603181Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7603531Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7603927Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7604328Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7604732Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7605100Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7605464Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7605859Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7606260Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7606631Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7607002Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7607347Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7607760Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7608169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7608541Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7608908Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7609245Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7609647Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7610044Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7610413Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7610778Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7611132Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7611549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7611940Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7612317Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7612689Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7613030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7613430Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7613818Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7614212Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7614589Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7614933Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7615330Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7615723Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7616098Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7616465Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7616815Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7617209Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7617637Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7618036Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7618399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7618748Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7619147Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7619548Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7619931Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7620296Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7620660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7621072Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7621471Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7621844Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7622221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7622570Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7622970Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7623367Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7623762Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7624219Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7624584Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7625003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7625418Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7625807Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7626182Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7626524Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7626948Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7627355Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7627738Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7628117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7628461Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7628867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7629265Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7629652Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7630038Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7630391Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7630832Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7631223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7631606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7631974Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7632320Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7632710Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7633106Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7633499Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7633884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7634231Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7634686Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7635095Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7635473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7635848Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7636801Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7637279Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7637698Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7638078Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7638453Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7638798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7639208Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7639606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7639981Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7640373Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7640736Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7641146Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7641540Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7641927Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7642301Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7642644Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7643048Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7643456Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7643846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7644237Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7644580Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7644991Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7645388Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7645775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7646142Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7646493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7646933Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7647344Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7647727Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7648092Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7648444Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7648841Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7649236Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7649617Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7650005Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7650371Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7650770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7651171Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7651547Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7651925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7652274Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7652667Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7653082Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7653472Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7653846Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7654187Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7654594Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7654994Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7655370Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7655746Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7656101Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7656507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7656913Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7657297Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7657672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7658012Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7658411Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7658798Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7659190Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7659572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7659922Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7660325Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7660713Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7661097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7661463Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7661810Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7662202Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7662618Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7663018Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7663385Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7664379Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7664799Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7665211Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7665606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7665980Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7666364Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7666781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7667177Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7667555Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7667933Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7668282Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7668680Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7669077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7669473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7669849Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7670205Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7670612Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7671013Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7671394Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7671775Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7672118Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7672542Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7672948Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7673332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7673706Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7674045Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7674445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7674838Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7675221Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7675587Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7675958Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7676380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7676769Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7677150Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7677517Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7677867Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7678259Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7678660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7679066Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7679458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7679809Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7680201Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7680605Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7680982Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7681356Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7681909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7682380Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7682781Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7683194Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7683572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7683914Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7684323Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7684726Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7685102Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7685502Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7685864Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7686269Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7686658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7687042Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7687419Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7687759Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7688163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7688554Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7688957Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7689345Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7689693Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7690097Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7690492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7691341Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7691716Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7692067Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7692492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7692909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7693293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7693660Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7694010Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7694409Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7694856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7695244Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7695648Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7695994Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7696400Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7696795Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7697170Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7697541Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7697887Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7698282Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7698696Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7699089Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7699462Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7699803Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7700204Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7700604Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7700981Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7701354Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7702290Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:10:00.7702859Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7703294Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7703697Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:10:00.7704092Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:10:00.7704501Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7704922Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7705327Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7705715Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7706138Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7706518Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7706920Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7707307Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7707701Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7708077Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7708433Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7708832Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7709254Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7709644Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7710032Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7710387Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:10:00.7710792Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7711202Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7711585Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:10:00.7711966Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:10:00.7712321Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:10:00.7712738Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7713160Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7713542Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:10:00.7713922Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:10:00.7714270Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:10:00.7714680Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:10:00.7715085Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:10:00.7715464Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:10:00.7715871Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:10:00.7716104Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:10:00.7716345Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:10:00.7716569Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:10:00.7716789Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:10:00.7717011Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:10:00.7717237Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:10:00.7717457Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:10:00.7717673Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:10:00.7717901Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:10:00.7718113Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:10:00.7718337Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:10:00.7718546Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:10:00.7718789Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:10:00.7719018Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:10:00.7719244Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:10:00.7719460Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:10:00.7719816Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:10:00.7720155Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:10:00.7720499Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:10:00.7720835Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:10:00.7721176Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:10:00.7721495Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:10:00.7721816Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:10:00.7722190Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:10:00.7722557Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:10:00.7722911Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:10:00.7723246Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:10:00.7723328Z         
2025-03-14T05:10:00.7723628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7724200Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7724277Z         
2025-03-14T05:10:00.7724556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7726263Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7726349Z         
2025-03-14T05:10:00.7726646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:10:00.7726793Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:10:00.7726869Z         
2025-03-14T05:10:00.7727239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:10:00.7727493Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:10:00.7727567Z         
2025-03-14T05:10:00.7727827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7728327Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7728409Z         
2025-03-14T05:10:00.7728686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7730474Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7730553Z         
2025-03-14T05:10:00.7730851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7730993Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:10:00.7731067Z         
2025-03-14T05:10:00.7731319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7731833Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7731917Z         
2025-03-14T05:10:00.7732190Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7733962Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7734031Z         
2025-03-14T05:10:00.7734325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7734465Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:10:00.7734535Z         
2025-03-14T05:10:00.7734787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7735291Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7735374Z         
2025-03-14T05:10:00.7735653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7738430Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7738516Z         
2025-03-14T05:10:00.7738802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7739310Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.7739383Z         
2025-03-14T05:10:00.7739659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7741508Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7741586Z         
2025-03-14T05:10:00.7741861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7742015Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:10:00.7742077Z         
2025-03-14T05:10:00.7742362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7742515Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:10:00.7742578Z         
2025-03-14T05:10:00.7742853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7743341Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7743430Z         
2025-03-14T05:10:00.7743694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7745604Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7745688Z         
2025-03-14T05:10:00.7745992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7746144Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:10:00.7746212Z         
2025-03-14T05:10:00.7746495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7746994Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7747067Z         
2025-03-14T05:10:00.7747322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7749108Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7749185Z         
2025-03-14T05:10:00.7749473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7749635Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:10:00.7749700Z         
2025-03-14T05:10:00.7749955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7750459Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7750536Z         
2025-03-14T05:10:00.7750802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7752577Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7752654Z         
2025-03-14T05:10:00.7752927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7753121Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:10:00.7753203Z         
2025-03-14T05:10:00.7753487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7753632Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:10:00.7753706Z         
2025-03-14T05:10:00.7753947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7754423Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7754497Z         
2025-03-14T05:10:00.7754754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7756480Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7756560Z         
2025-03-14T05:10:00.7756859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7757004Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:10:00.7757067Z         
2025-03-14T05:10:00.7757317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7757792Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7757865Z         
2025-03-14T05:10:00.7758121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7759865Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7759951Z         
2025-03-14T05:10:00.7760232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7760375Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:10:00.7760438Z         
2025-03-14T05:10:00.7760687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7761166Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7761237Z         
2025-03-14T05:10:00.7761493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7763236Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7763327Z         
2025-03-14T05:10:00.7763603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7763766Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:10:00.7763830Z         
2025-03-14T05:10:00.7764111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7764263Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:10:00.7764336Z         
2025-03-14T05:10:00.7764579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7765063Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7765126Z         
2025-03-14T05:10:00.7765392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7767151Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7767231Z         
2025-03-14T05:10:00.7767514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7767657Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:10:00.7767729Z         
2025-03-14T05:10:00.7767968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7768451Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7768521Z         
2025-03-14T05:10:00.7768777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7770533Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7770601Z         
2025-03-14T05:10:00.7770886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7771034Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:10:00.7771096Z         
2025-03-14T05:10:00.7771350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7771833Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7771905Z         
2025-03-14T05:10:00.7772178Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7773918Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7773990Z         
2025-03-14T05:10:00.7774235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7774725Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.7774788Z         
2025-03-14T05:10:00.7775048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7776859Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7776941Z         
2025-03-14T05:10:00.7777225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7777373Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:10:00.7777446Z         
2025-03-14T05:10:00.7777723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7777876Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:10:00.7777939Z         
2025-03-14T05:10:00.7778187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7778672Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7778762Z         
2025-03-14T05:10:00.7779020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7780747Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7780822Z         
2025-03-14T05:10:00.7781104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7781252Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:10:00.7781316Z         
2025-03-14T05:10:00.7781741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7782234Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7782366Z         
2025-03-14T05:10:00.7782636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7784468Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7784554Z         
2025-03-14T05:10:00.7784841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7784988Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:10:00.7785061Z         
2025-03-14T05:10:00.7785311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7785838Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7785929Z         
2025-03-14T05:10:00.7786208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7788000Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7788078Z         
2025-03-14T05:10:00.7788365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7788522Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:10:00.7788611Z         
2025-03-14T05:10:00.7788894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7789050Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:10:00.7789116Z         
2025-03-14T05:10:00.7789374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7789946Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7790021Z         
2025-03-14T05:10:00.7790284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7792068Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7792142Z         
2025-03-14T05:10:00.7792445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7792608Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:10:00.7792673Z         
2025-03-14T05:10:00.7792933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7793420Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7793494Z         
2025-03-14T05:10:00.7793763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7795541Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7795634Z         
2025-03-14T05:10:00.7795922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7796072Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:10:00.7796152Z         
2025-03-14T05:10:00.7796412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7796903Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7796977Z         
2025-03-14T05:10:00.7797249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7799035Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7799125Z         
2025-03-14T05:10:00.7799412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7799568Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:10:00.7799640Z         
2025-03-14T05:10:00.7799916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7800072Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:10:00.7800137Z         
2025-03-14T05:10:00.7800395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7800879Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7800951Z         
2025-03-14T05:10:00.7801234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7803002Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7803092Z         
2025-03-14T05:10:00.7803379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7803528Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:10:00.7803592Z         
2025-03-14T05:10:00.7803849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7804344Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7804414Z         
2025-03-14T05:10:00.7804670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7806399Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7806492Z         
2025-03-14T05:10:00.7806770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7806916Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:10:00.7806982Z         
2025-03-14T05:10:00.7807232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7807720Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7807793Z         
2025-03-14T05:10:00.7808059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7809824Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7809948Z         
2025-03-14T05:10:00.7810221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7810383Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:10:00.7810451Z         
2025-03-14T05:10:00.7810739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7810890Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:10:00.7810964Z         
2025-03-14T05:10:00.7811209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7811688Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7811780Z         
2025-03-14T05:10:00.7812052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7813799Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7813873Z         
2025-03-14T05:10:00.7814153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7814294Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:10:00.7814357Z         
2025-03-14T05:10:00.7814609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7815079Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7815175Z         
2025-03-14T05:10:00.7815434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7817191Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7817265Z         
2025-03-14T05:10:00.7817545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7828127Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:10:00.7828250Z         
2025-03-14T05:10:00.7828557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7829117Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7829217Z         
2025-03-14T05:10:00.7829488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7831273Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7831349Z         
2025-03-14T05:10:00.7831599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7832097Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.7832182Z         
2025-03-14T05:10:00.7832454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7834315Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7834385Z         
2025-03-14T05:10:00.7834681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7834820Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:10:00.7834894Z         
2025-03-14T05:10:00.7835175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7835326Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:10:00.7835392Z         
2025-03-14T05:10:00.7835666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7836165Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7836230Z         
2025-03-14T05:10:00.7836501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7838694Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7838775Z         
2025-03-14T05:10:00.7839061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7839194Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:10:00.7839285Z         
2025-03-14T05:10:00.7839532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7840025Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7840089Z         
2025-03-14T05:10:00.7840355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7842079Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7842153Z         
2025-03-14T05:10:00.7842438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7842586Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:10:00.7842671Z         
2025-03-14T05:10:00.7842915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7843396Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7843460Z         
2025-03-14T05:10:00.7843722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7845439Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7845507Z         
2025-03-14T05:10:00.7845806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7845951Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:10:00.7846022Z         
2025-03-14T05:10:00.7846297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7846458Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:10:00.7846522Z         
2025-03-14T05:10:00.7846773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7847233Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7847307Z         
2025-03-14T05:10:00.7847567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7849311Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7849410Z         
2025-03-14T05:10:00.7849690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7849830Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:10:00.7849894Z         
2025-03-14T05:10:00.7850147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7850624Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7850698Z         
2025-03-14T05:10:00.7850965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7852689Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7852780Z         
2025-03-14T05:10:00.7853082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7853214Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:10:00.7853286Z         
2025-03-14T05:10:00.7853533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7854016Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7854080Z         
2025-03-14T05:10:00.7854346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7856070Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7856159Z         
2025-03-14T05:10:00.7856440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7856582Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:10:00.7856652Z         
2025-03-14T05:10:00.7856951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7857100Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:10:00.7857164Z         
2025-03-14T05:10:00.7857415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7857876Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7857949Z         
2025-03-14T05:10:00.7858204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7859958Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7860046Z         
2025-03-14T05:10:00.7860326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7860463Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:10:00.7860527Z         
2025-03-14T05:10:00.7860780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7861255Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7861328Z         
2025-03-14T05:10:00.7861584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7863337Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7863426Z         
2025-03-14T05:10:00.7863708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7863853Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:10:00.7863922Z         
2025-03-14T05:10:00.7864255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7864775Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7864852Z         
2025-03-14T05:10:00.7865166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7867012Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7867089Z         
2025-03-14T05:10:00.7867370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7867522Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:10:00.7867598Z         
2025-03-14T05:10:00.7867878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7868025Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:10:00.7868088Z         
2025-03-14T05:10:00.7868339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7868845Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7868932Z         
2025-03-14T05:10:00.7869195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7870971Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7871045Z         
2025-03-14T05:10:00.7871326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7871469Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:10:00.7871534Z         
2025-03-14T05:10:00.7871791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7872298Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7872370Z         
2025-03-14T05:10:00.7872650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7874411Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7874484Z         
2025-03-14T05:10:00.7874766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7874905Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:10:00.7874970Z         
2025-03-14T05:10:00.7875251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7875749Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7875823Z         
2025-03-14T05:10:00.7876084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7877863Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7877939Z         
2025-03-14T05:10:00.7878218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7878384Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:10:00.7878448Z         
2025-03-14T05:10:00.7878750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7878888Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:10:00.7878959Z         
2025-03-14T05:10:00.7879214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7879683Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7879756Z         
2025-03-14T05:10:00.7880012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7881987Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7882082Z         
2025-03-14T05:10:00.7882372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7882513Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:10:00.7882579Z         
2025-03-14T05:10:00.7882832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7883298Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7883373Z         
2025-03-14T05:10:00.7883632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7885357Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7885451Z         
2025-03-14T05:10:00.7885727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7885888Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:10:00.7885951Z         
2025-03-14T05:10:00.7886202Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7886668Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7886740Z         
2025-03-14T05:10:00.7886997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7888777Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7888863Z         
2025-03-14T05:10:00.7910591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7910856Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:10:00.7910934Z         
2025-03-14T05:10:00.7911264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7911416Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:10:00.7911505Z         
2025-03-14T05:10:00.7911791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7912317Z         x_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7912387Z         
2025-03-14T05:10:00.7912683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7914636Z         x_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7914746Z         
2025-03-14T05:10:00.7915050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7915194Z         out_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:10:00.7915273Z         
2025-03-14T05:10:00.7915534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7916043Z         x_90: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7916120Z         
2025-03-14T05:10:00.7916387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7918194Z         x_91: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7918293Z         
2025-03-14T05:10:00.7918592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7918741Z         out_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:10:00.7918809Z         
2025-03-14T05:10:00.7919073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7940072Z         x_92: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7940206Z         
2025-03-14T05:10:00.7940512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7942455Z         x_93: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_6_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7942540Z         
2025-03-14T05:10:00.7942864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7943026Z         x_93 += out_51;  out_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_93;  x_93 = out_51 = None
2025-03-14T05:10:00.7943095Z         
2025-03-14T05:10:00.7943402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7943550Z         out_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:10:00.7943628Z         
2025-03-14T05:10:00.7943887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7944492Z         x_94: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7944912Z         
2025-03-14T05:10:00.7945196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7946974Z         x_95: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7947045Z         
2025-03-14T05:10:00.7947351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7947494Z         out_56: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_95);  x_95 = None
2025-03-14T05:10:00.7947569Z         
2025-03-14T05:10:00.7947830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7948328Z         x_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7948413Z         
2025-03-14T05:10:00.7948689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7950487Z         x_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7950558Z         
2025-03-14T05:10:00.7951672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7951817Z         out_57: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:10:00.7951890Z         
2025-03-14T05:10:00.7952136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7952650Z         x_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7952741Z         
2025-03-14T05:10:00.7953000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7954737Z         x_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_7_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7954805Z         
2025-03-14T05:10:00.7955088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7955235Z         x_99 += out_55;  out_58: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_99;  x_99 = out_55 = None
2025-03-14T05:10:00.7955307Z         
2025-03-14T05:10:00.7955600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7955749Z         out_59: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:10:00.7955823Z         
2025-03-14T05:10:00.7956067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7956560Z         x_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7956626Z         
2025-03-14T05:10:00.7956891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7958694Z         x_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7959438Z         
2025-03-14T05:10:00.7959814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7959992Z         out_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_101);  x_101 = None
2025-03-14T05:10:00.7960068Z         
2025-03-14T05:10:00.7960322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7960812Z         x_102: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7960878Z         
2025-03-14T05:10:00.7961144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7963489Z         x_103: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7963588Z         
2025-03-14T05:10:00.7963957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7964097Z         out_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:10:00.7964172Z         
2025-03-14T05:10:00.7964436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7964921Z         x_104: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7964988Z         
2025-03-14T05:10:00.7965257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7967015Z         x_105: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_8_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7967098Z         
2025-03-14T05:10:00.7967378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7967530Z         x_105 += out_59;  out_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_105;  x_105 = out_59 = None
2025-03-14T05:10:00.7967601Z         
2025-03-14T05:10:00.7967877Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7968024Z         out_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:10:00.7968087Z         
2025-03-14T05:10:00.7968339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7968806Z         x_106: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7968876Z         
2025-03-14T05:10:00.7969141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7970927Z         x_107: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7971017Z         
2025-03-14T05:10:00.7971295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7971441Z         out_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_107);  x_107 = None
2025-03-14T05:10:00.7971513Z         
2025-03-14T05:10:00.7971757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7972237Z         x_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7972300Z         
2025-03-14T05:10:00.7972564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7974321Z         x_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7974410Z         
2025-03-14T05:10:00.7974694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7974830Z         out_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_109);  x_109 = None
2025-03-14T05:10:00.7974902Z         
2025-03-14T05:10:00.7975146Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7975629Z         x_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_65, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_65 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7975693Z         
2025-03-14T05:10:00.7975956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7977703Z         x_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_110, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_110 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_9_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7977785Z         
2025-03-14T05:10:00.7978101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7978258Z         x_111 += out_63;  out_66: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_111;  x_111 = out_63 = None
2025-03-14T05:10:00.7978329Z         
2025-03-14T05:10:00.7978601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7978747Z         out_67: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_66);  out_66 = None
2025-03-14T05:10:00.7978809Z         
2025-03-14T05:10:00.7979058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7979526Z         x_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_67, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7979601Z         
2025-03-14T05:10:00.7979876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7981836Z         x_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7981920Z         
2025-03-14T05:10:00.7982199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7982341Z         out_68: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_113);  x_113 = None
2025-03-14T05:10:00.7982405Z         
2025-03-14T05:10:00.7982666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7983149Z         x_114: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7983303Z         
2025-03-14T05:10:00.7983574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7985464Z         x_115: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_114, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_114 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7985545Z         
2025-03-14T05:10:00.7985843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7985978Z         out_69: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_115);  x_115 = None
2025-03-14T05:10:00.7986052Z         
2025-03-14T05:10:00.7986303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7986821Z         x_116: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_69, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_69 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7986913Z         
2025-03-14T05:10:00.7987185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7988976Z         x_117: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_10_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7989056Z         
2025-03-14T05:10:00.7989346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.7989496Z         x_117 += out_67;  out_70: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_117;  x_117 = out_67 = None
2025-03-14T05:10:00.7989569Z         
2025-03-14T05:10:00.7989853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7990022Z         out_71: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_70);  out_70 = None
2025-03-14T05:10:00.7990088Z         
2025-03-14T05:10:00.7990348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7990842Z         x_118: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_71, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.7990915Z         
2025-03-14T05:10:00.7991179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7992963Z         x_119: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_118, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_118 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7993039Z         
2025-03-14T05:10:00.7993337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7993506Z         out_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_119);  x_119 = None
2025-03-14T05:10:00.7993571Z         
2025-03-14T05:10:00.7993828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7994317Z         x_120: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.7994389Z         
2025-03-14T05:10:00.7994649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7996424Z         x_121: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_120, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_120 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7996516Z         
2025-03-14T05:10:00.7996798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.7996943Z         out_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_121);  x_121 = None
2025-03-14T05:10:00.7997008Z         
2025-03-14T05:10:00.7997283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.7997787Z         x_122: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_73, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_73 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.7997851Z         
2025-03-14T05:10:00.7998122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.7999890Z         x_123: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_122, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_122 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_11_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.7999980Z         
2025-03-14T05:10:00.8000262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8000413Z         x_123 += out_71;  out_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_123;  x_123 = out_71 = None
2025-03-14T05:10:00.8000486Z         
2025-03-14T05:10:00.8000759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8000906Z         out_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_74);  out_74 = None
2025-03-14T05:10:00.8000969Z         
2025-03-14T05:10:00.8001219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8001685Z         x_124: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_75, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8001758Z         
2025-03-14T05:10:00.8002014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8003752Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_124, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_124 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8003840Z         
2025-03-14T05:10:00.8004118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8004257Z         out_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_125);  x_125 = None
2025-03-14T05:10:00.8004319Z         
2025-03-14T05:10:00.8004572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8005040Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8005113Z         
2025-03-14T05:10:00.8005366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8007115Z         x_127: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_126, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_126 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8007206Z         
2025-03-14T05:10:00.8007485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8007624Z         out_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_127);  x_127 = None
2025-03-14T05:10:00.8007688Z         
2025-03-14T05:10:00.8007941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8008413Z         x_128: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_77, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_77 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8008483Z         
2025-03-14T05:10:00.8008740Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8010474Z         x_129: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_128, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_128 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_12_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8010561Z         
2025-03-14T05:10:00.8010833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8010987Z         x_129 += out_75;  out_78: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_129;  x_129 = out_75 = None
2025-03-14T05:10:00.8011053Z         
2025-03-14T05:10:00.8011335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8011470Z         out_79: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_78);  out_78 = None
2025-03-14T05:10:00.8011554Z         
2025-03-14T05:10:00.8014156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8014662Z         x_130: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_79, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8014749Z         
2025-03-14T05:10:00.8015057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8016829Z         x_131: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_130, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_130 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8016906Z         
2025-03-14T05:10:00.8017189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8017337Z         out_80: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_131);  x_131 = None
2025-03-14T05:10:00.8017403Z         
2025-03-14T05:10:00.8017659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8018182Z         x_132: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8018284Z         
2025-03-14T05:10:00.8018552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8020345Z         x_133: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_132, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_132 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8020424Z         
2025-03-14T05:10:00.8020712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8020855Z         out_81: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_133);  x_133 = None
2025-03-14T05:10:00.8020919Z         
2025-03-14T05:10:00.8021174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8021722Z         x_134: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_81, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_81 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8021814Z         
2025-03-14T05:10:00.8022074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8023920Z         x_135: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_134, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_134 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_13_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8024023Z         
2025-03-14T05:10:00.8024404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8024568Z         x_135 += out_79;  out_82: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_135;  x_135 = out_79 = None
2025-03-14T05:10:00.8024635Z         
2025-03-14T05:10:00.8024928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8025097Z         out_83: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_82);  out_82 = None
2025-03-14T05:10:00.8025173Z         
2025-03-14T05:10:00.8025424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8025930Z         x_136: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_83, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8026005Z         
2025-03-14T05:10:00.8026266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8028048Z         x_137: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_136, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_136 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8028114Z         
2025-03-14T05:10:00.8028404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8028561Z         out_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_137);  x_137 = None
2025-03-14T05:10:00.8028641Z         
2025-03-14T05:10:00.8028896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8040635Z         x_138: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8040748Z         
2025-03-14T05:10:00.8041023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8042783Z         x_139: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_138, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_138 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8042928Z         
2025-03-14T05:10:00.8043214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8043357Z         out_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_139);  x_139 = None
2025-03-14T05:10:00.8043423Z         
2025-03-14T05:10:00.8043704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8044177Z         x_140: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_85, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_85 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8044248Z         
2025-03-14T05:10:00.8044507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8047153Z         x_141: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_140, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_140 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_14_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8047286Z         
2025-03-14T05:10:00.8047622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8047782Z         x_141 += out_83;  out_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_141;  x_141 = out_83 = None
2025-03-14T05:10:00.8047848Z         
2025-03-14T05:10:00.8048137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8048276Z         out_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_86);  out_86 = None
2025-03-14T05:10:00.8048346Z         
2025-03-14T05:10:00.8048588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8049075Z         x_142: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_87, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8049141Z         
2025-03-14T05:10:00.8049409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8053299Z         x_143: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_142, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_142 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8053416Z         
2025-03-14T05:10:00.8053744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8053888Z         out_88: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_143);  x_143 = None
2025-03-14T05:10:00.8053964Z         
2025-03-14T05:10:00.8054219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8054715Z         x_144: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_88, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_88 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8054790Z         
2025-03-14T05:10:00.8055056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8056906Z         x_145: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_144, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_144 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8056999Z         
2025-03-14T05:10:00.8057283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8057428Z         out_89: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_145);  x_145 = None
2025-03-14T05:10:00.8057494Z         
2025-03-14T05:10:00.8057751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8058274Z         x_146: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_89, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_89 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8058349Z         
2025-03-14T05:10:00.8058610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8061428Z         x_147: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_146, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_146 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_15_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8061553Z         
2025-03-14T05:10:00.8061862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8062026Z         x_147 += out_87;  out_90: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_147;  x_147 = out_87 = None
2025-03-14T05:10:00.8062094Z         
2025-03-14T05:10:00.8062385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8062528Z         out_91: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_90);  out_90 = None
2025-03-14T05:10:00.8062602Z         
2025-03-14T05:10:00.8062848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8063341Z         x_148: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_91, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8063408Z         
2025-03-14T05:10:00.8063692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8065607Z         x_149: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_148, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_148 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8065682Z         
2025-03-14T05:10:00.8065978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8066117Z         out_92: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_149);  x_149 = None
2025-03-14T05:10:00.8066191Z         
2025-03-14T05:10:00.8066443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8066944Z         x_150: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_92, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_92 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8067034Z         
2025-03-14T05:10:00.8067313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8069125Z         x_151: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_150, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_150 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8069196Z         
2025-03-14T05:10:00.8069495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8069632Z         out_93: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_151);  x_151 = None
2025-03-14T05:10:00.8069704Z         
2025-03-14T05:10:00.8069959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8070483Z         x_152: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_93, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_93 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8070572Z         
2025-03-14T05:10:00.8070841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8072647Z         x_153: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_152, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_152 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_16_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8072723Z         
2025-03-14T05:10:00.8073009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8073168Z         x_153 += out_91;  out_94: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_153;  x_153 = out_91 = None
2025-03-14T05:10:00.8073233Z         
2025-03-14T05:10:00.8073528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8073687Z         out_95: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_94);  out_94 = None
2025-03-14T05:10:00.8073763Z         
2025-03-14T05:10:00.8074015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8074524Z         x_154: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_95, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8074589Z         
2025-03-14T05:10:00.8074853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8076572Z         x_155: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_154, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_154 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8076645Z         
2025-03-14T05:10:00.8076929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8077082Z         out_96: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_155);  x_155 = None
2025-03-14T05:10:00.8077168Z         
2025-03-14T05:10:00.8077414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8077891Z         x_156: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_96, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_96 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8077955Z         
2025-03-14T05:10:00.8078217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8079936Z         x_157: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_156, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_156 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8080018Z         
2025-03-14T05:10:00.8080310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8080443Z         out_97: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_157);  x_157 = None
2025-03-14T05:10:00.8080514Z         
2025-03-14T05:10:00.8080772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8081289Z         x_158: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_97, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_97 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8081357Z         
2025-03-14T05:10:00.8081825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8083570Z         x_159: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_158, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_158 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_17_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8083707Z         
2025-03-14T05:10:00.8084016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8084162Z         x_159 += out_95;  out_98: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_159;  x_159 = out_95 = None
2025-03-14T05:10:00.8084234Z         
2025-03-14T05:10:00.8084507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8084651Z         out_99: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_98);  out_98 = None
2025-03-14T05:10:00.8084713Z         
2025-03-14T05:10:00.8084962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8085438Z         x_160: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_99, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8085503Z         
2025-03-14T05:10:00.8085769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8106009Z         x_161: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_160, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_160 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8106232Z         
2025-03-14T05:10:00.8106607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8106774Z         out_100: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_161);  x_161 = None
2025-03-14T05:10:00.8106864Z         
2025-03-14T05:10:00.8107181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8107772Z         x_162: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_100, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_100 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8107855Z         
2025-03-14T05:10:00.8108201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8110263Z         x_163: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_162, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_162 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8110434Z         
2025-03-14T05:10:00.8110806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8110966Z         out_101: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_163);  x_163 = None
2025-03-14T05:10:00.8111055Z         
2025-03-14T05:10:00.8111378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8111997Z         x_164: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_101, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_101 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8112077Z         
2025-03-14T05:10:00.8112422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8114434Z         x_165: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_164, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_164 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_18_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8114545Z         
2025-03-14T05:10:00.8114905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8115087Z         x_165 += out_99;  out_102: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_165;  x_165 = out_99 = None
2025-03-14T05:10:00.8115176Z         
2025-03-14T05:10:00.8115532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8115701Z         out_103: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_102);  out_102 = None
2025-03-14T05:10:00.8115768Z         
2025-03-14T05:10:00.8116064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8116640Z         x_166: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_103, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8116720Z         
2025-03-14T05:10:00.8117047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8118858Z         x_167: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_166, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_166 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8118936Z         
2025-03-14T05:10:00.8119213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8119358Z         out_104: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_167);  x_167 = None
2025-03-14T05:10:00.8119427Z         
2025-03-14T05:10:00.8119669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8120161Z         x_168: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_104, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_104 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8120240Z         
2025-03-14T05:10:00.8120508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8122303Z         x_169: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_168, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_168 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8122380Z         
2025-03-14T05:10:00.8122667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8122805Z         out_105: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_169);  x_169 = None
2025-03-14T05:10:00.8122876Z         
2025-03-14T05:10:00.8123119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8123625Z         x_170: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_105, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_105 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8123704Z         
2025-03-14T05:10:00.8123975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8125800Z         x_171: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_170, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_170 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_19_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8125870Z         
2025-03-14T05:10:00.8126159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8126328Z         x_171 += out_103;  out_106: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_171;  x_171 = out_103 = None
2025-03-14T05:10:00.8126399Z         
2025-03-14T05:10:00.8126673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8126840Z         out_107: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_106);  out_106 = None
2025-03-14T05:10:00.8126906Z         
2025-03-14T05:10:00.8127164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8127661Z         x_172: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_107, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8127738Z         
2025-03-14T05:10:00.8127995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8129766Z         x_173: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_172, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_172 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8129840Z         
2025-03-14T05:10:00.8130115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8130274Z         out_108: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_173);  x_173 = None
2025-03-14T05:10:00.8130360Z         
2025-03-14T05:10:00.8130612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8131097Z         x_174: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_108, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_108 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8131167Z         
2025-03-14T05:10:00.8131436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8133215Z         x_175: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_174, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_174 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8133307Z         
2025-03-14T05:10:00.8133613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8133749Z         out_109: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_175);  x_175 = None
2025-03-14T05:10:00.8133819Z         
2025-03-14T05:10:00.8134076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8134570Z         x_176: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_109, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_109 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8134634Z         
2025-03-14T05:10:00.8134895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8136660Z         x_177: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_176, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_176 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_20_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8136749Z         
2025-03-14T05:10:00.8137045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8137202Z         x_177 += out_107;  out_110: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_177;  x_177 = out_107 = None
2025-03-14T05:10:00.8137275Z         
2025-03-14T05:10:00.8137550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8137699Z         out_111: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_110);  out_110 = None
2025-03-14T05:10:00.8137762Z         
2025-03-14T05:10:00.8138013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8138490Z         x_178: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_111, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8138564Z         
2025-03-14T05:10:00.8138821Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8140675Z         x_179: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_178, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_178 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8140771Z         
2025-03-14T05:10:00.8141048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8141194Z         out_112: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_179);  x_179 = None
2025-03-14T05:10:00.8141257Z         
2025-03-14T05:10:00.8141508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8141996Z         x_180: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_112, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_112 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8142071Z         
2025-03-14T05:10:00.8142330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8144212Z         x_181: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_180, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_180 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8144314Z         
2025-03-14T05:10:00.8144604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8144753Z         out_113: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_181);  x_181 = None
2025-03-14T05:10:00.8144820Z         
2025-03-14T05:10:00.8145080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8145602Z         x_182: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_113, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_113 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8145666Z         
2025-03-14T05:10:00.8145934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8147804Z         x_183: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_182, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_182 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_21_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8147896Z         
2025-03-14T05:10:00.8148184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8148346Z         x_183 += out_111;  out_114: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_183;  x_183 = out_111 = None
2025-03-14T05:10:00.8148418Z         
2025-03-14T05:10:00.8148703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8148857Z         out_115: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_114);  out_114 = None
2025-03-14T05:10:00.8148923Z         
2025-03-14T05:10:00.8149181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8149676Z         x_184: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_115, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8149749Z         
2025-03-14T05:10:00.8150033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8151901Z         x_185: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_184, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_184 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8151977Z         
2025-03-14T05:10:00.8152268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8152417Z         out_116: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_185);  x_185 = None
2025-03-14T05:10:00.8152482Z         
2025-03-14T05:10:00.8152742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8153242Z         x_186: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_116, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_116 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8153336Z         
2025-03-14T05:10:00.8153605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8155467Z         x_187: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_186, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_186 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8155545Z         
2025-03-14T05:10:00.8155832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8155980Z         out_117: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_187);  x_187 = None
2025-03-14T05:10:00.8156046Z         
2025-03-14T05:10:00.8156305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8156831Z         x_188: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_117, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_117 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8156920Z         
2025-03-14T05:10:00.8157189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8159031Z         x_189: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_188, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_188 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_22_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8159109Z         
2025-03-14T05:10:00.8159399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8159559Z         x_189 += out_115;  out_118: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_189;  x_189 = out_115 = None
2025-03-14T05:10:00.8159621Z         
2025-03-14T05:10:00.8159908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8160077Z         out_119: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_118);  out_118 = None
2025-03-14T05:10:00.8160142Z         
2025-03-14T05:10:00.8160386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8160886Z         x_190: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8160957Z         
2025-03-14T05:10:00.8161222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8162973Z         x_191: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_190, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_190 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8163047Z         
2025-03-14T05:10:00.8163326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8163523Z         out_120: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_191);  x_191 = None
2025-03-14T05:10:00.8163603Z         
2025-03-14T05:10:00.8163852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8164334Z         x_192: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_120, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_120 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8164404Z         
2025-03-14T05:10:00.8164662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8168400Z         x_193: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_192, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_192 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8168559Z         
2025-03-14T05:10:00.8168875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8174309Z         out_121: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_193);  x_193 = None
2025-03-14T05:10:00.8174402Z         
2025-03-14T05:10:00.8174748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8175254Z         x_194: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_121, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_121 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8175329Z         
2025-03-14T05:10:00.8175594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8177898Z         x_195: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_194, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_194 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8177992Z         
2025-03-14T05:10:00.8178280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8178819Z         x_196: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:10:00.8178887Z         
2025-03-14T05:10:00.8179158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8181042Z         x_197: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_196, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_196 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8181112Z         
2025-03-14T05:10:00.8181404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8181839Z         x_195 += x_197;  out_122: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_195;  x_195 = x_197 = None
2025-03-14T05:10:00.8181919Z         
2025-03-14T05:10:00.8182210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8182429Z         out_123: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_122);  out_122 = None
2025-03-14T05:10:00.8182496Z         
2025-03-14T05:10:00.8182760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8188134Z         x_198: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_123, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8188222Z         
2025-03-14T05:10:00.8188526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8190424Z         x_199: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_198, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_198 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8190525Z         
2025-03-14T05:10:00.8190829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8190971Z         out_124: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_199);  x_199 = None
2025-03-14T05:10:00.8191044Z         
2025-03-14T05:10:00.8191303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8191808Z         x_200: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_124, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_124 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8191877Z         
2025-03-14T05:10:00.8192149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8193905Z         x_201: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_200, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_200 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8194030Z         
2025-03-14T05:10:00.8194333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8194470Z         out_125: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_201);  x_201 = None
2025-03-14T05:10:00.8194540Z         
2025-03-14T05:10:00.8194786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8195287Z         x_202: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_125, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_125 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8195354Z         
2025-03-14T05:10:00.8195621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8197402Z         x_203: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_202, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_202 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8197480Z         
2025-03-14T05:10:00.8197767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8197928Z         x_203 += out_123;  out_126: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_203;  x_203 = out_123 = None
2025-03-14T05:10:00.8198001Z         
2025-03-14T05:10:00.8198279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8198431Z         out_127: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_126);  out_126 = None
2025-03-14T05:10:00.8198497Z         
2025-03-14T05:10:00.8198749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8199228Z         x_204: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_127, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:10:00.8199300Z         
2025-03-14T05:10:00.8199565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8201353Z         x_205: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_204, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_204 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8201443Z         
2025-03-14T05:10:00.8201724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8201867Z         out_128: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_205);  x_205 = None
2025-03-14T05:10:00.8201930Z         
2025-03-14T05:10:00.8202180Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8202673Z         x_206: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_128, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_128 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:10:00.8202736Z         
2025-03-14T05:10:00.8203001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8204759Z         x_207: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_206, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_206 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8204849Z         
2025-03-14T05:10:00.8205145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8205280Z         out_129: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_207);  x_207 = None
2025-03-14T05:10:00.8205348Z         
2025-03-14T05:10:00.8205594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8206092Z         x_208: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_129, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_129 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:10:00.8206158Z         
2025-03-14T05:10:00.8206438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:10:00.8208213Z         x_209: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_208, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_208 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:10:00.8208289Z         
2025-03-14T05:10:00.8208571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:10:00.8208727Z         x_209 += out_127;  out_130: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_209;  x_209 = out_127 = None
2025-03-14T05:10:00.8208800Z         
2025-03-14T05:10:00.8249420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:10:00.8249708Z         out_131: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_130);  out_130 = None
2025-03-14T05:10:00.8249789Z         
2025-03-14T05:10:00.8250123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8250906Z         x_210: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_131, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_131 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.8251070Z         
2025-03-14T05:10:00.8251407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8252028Z         x_211: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_210, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:10:00.8252117Z         
2025-03-14T05:10:00.8252576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.8252911Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_210, scale_factor = 2.0, mode = 'nearest');  x_210 = None
2025-03-14T05:10:00.8253001Z         
2025-03-14T05:10:00.8253314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8253940Z         x_212: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_119, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_119 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.8254071Z         
2025-03-14T05:10:00.8254482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.8254737Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_212 + top_down_features;  x_212 = top_down_features = None
2025-03-14T05:10:00.8254826Z         
2025-03-14T05:10:00.8255150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8255808Z         x_213: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:10:00.8255884Z         
2025-03-14T05:10:00.8256370Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.8256761Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:10:00.8256849Z         
2025-03-14T05:10:00.8257129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8257771Z         x_214: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.8257852Z         
2025-03-14T05:10:00.8258306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.8258573Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_214 + top_down_features_1;  x_214 = top_down_features_1 = None
2025-03-14T05:10:00.8258660Z         
2025-03-14T05:10:00.8258956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8259612Z         x_215: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:10:00.8259681Z         
2025-03-14T05:10:00.8260143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:10:00.8260497Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:10:00.8260565Z         
2025-03-14T05:10:00.8260849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8261450Z         x_216: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.8261540Z         
2025-03-14T05:10:00.8261881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:10:00.8262118Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_216 + top_down_features_2;  x_216 = top_down_features_2 = None
2025-03-14T05:10:00.8262183Z         
2025-03-14T05:10:00.8262442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8263072Z         x_217: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:10:00.8263147Z         
2025-03-14T05:10:00.8263514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:10:00.8263738Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_211, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:10:00.8263803Z         
2025-03-14T05:10:00.8264345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8264520Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.8264590Z         
2025-03-14T05:10:00.8264919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8265092Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:00.8265165Z         
2025-03-14T05:10:00.8265600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8265765Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:10:00.8265830Z         
2025-03-14T05:10:00.8266130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8266273Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:10:00.8266347Z         
2025-03-14T05:10:00.8266725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.8266915Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:10:00.8267017Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:10:00.8267149Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:10:00.8267215Z         
2025-03-14T05:10:00.8267552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.8267684Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:10:00.8267756Z         
2025-03-14T05:10:00.8268112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.8268241Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:10:00.8268305Z         
2025-03-14T05:10:00.8268712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.8268926Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:10:00.8268999Z         
2025-03-14T05:10:00.8269422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.8269560Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:10:00.8269993Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:10:00.8270131Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:10:00.8270253Z         x_218: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:10:00.8270325Z         
2025-03-14T05:10:00.8270753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8270912Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.8270985Z         
2025-03-14T05:10:00.8271292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8271452Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:10:00.8271517Z         
2025-03-14T05:10:00.8271952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8272100Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:10:00.8272172Z         
2025-03-14T05:10:00.8272462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8272608Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:10:00.8272674Z         
2025-03-14T05:10:00.8273059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.8273256Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:10:00.8273371Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:10:00.8273500Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:10:00.8273572Z         
2025-03-14T05:10:00.8273900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.8274040Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:10:00.8274120Z         
2025-03-14T05:10:00.8274456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.8274585Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:10:00.8274709Z         
2025-03-14T05:10:00.8275114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.8275340Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:10:00.8275406Z         
2025-03-14T05:10:00.8275834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.8275969Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:10:00.8276401Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:10:00.8276528Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:10:00.8276655Z         x_219: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:10:00.8276720Z         
2025-03-14T05:10:00.8277160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8277317Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.8277385Z         
2025-03-14T05:10:00.8277700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8277860Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:10:00.8277933Z         
2025-03-14T05:10:00.8278359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8278511Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:10:00.8278576Z         
2025-03-14T05:10:00.8278871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8279008Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:10:00.8279081Z         
2025-03-14T05:10:00.8279452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.8279655Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:10:00.8279756Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:10:00.8279886Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:10:00.8279949Z         
2025-03-14T05:10:00.8280279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.8280407Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:10:00.8280492Z         
2025-03-14T05:10:00.8280816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.8280947Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:10:00.8281011Z         
2025-03-14T05:10:00.8281410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.8281857Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:10:00.8281937Z         
2025-03-14T05:10:00.8282352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.8282495Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:10:00.8282916Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:10:00.8283048Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:10:00.8283163Z         x_220: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:10:00.8283235Z         
2025-03-14T05:10:00.8283660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8283816Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.8283943Z         
2025-03-14T05:10:00.8284257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8284401Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:10:00.8284466Z         
2025-03-14T05:10:00.8284904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8285049Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:10:00.8285122Z         
2025-03-14T05:10:00.8285415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8285562Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:10:00.8285628Z         
2025-03-14T05:10:00.8286007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.8286202Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:10:00.8286314Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:10:00.8286435Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:10:00.8286509Z         
2025-03-14T05:10:00.8286837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.8286990Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:10:00.8287056Z         
2025-03-14T05:10:00.8287391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.8287512Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:10:00.8287585Z         
2025-03-14T05:10:00.8287984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.8288206Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:10:00.8288271Z         
2025-03-14T05:10:00.8288696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.8288826Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:10:00.8289252Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:10:00.8289375Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:10:00.8289498Z         x_221: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:10:00.8289564Z         
2025-03-14T05:10:00.8290001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8290171Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.8290254Z         
2025-03-14T05:10:00.8290556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8290696Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:10:00.8290771Z         
2025-03-14T05:10:00.8291199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:00.8291352Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:00.8291416Z         
2025-03-14T05:10:00.8291720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8291859Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:10:00.8291931Z         
2025-03-14T05:10:00.8292300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:00.8292503Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:10:00.8292606Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:10:00.8292730Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:10:00.8292796Z         
2025-03-14T05:10:00.8293132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:00.8293276Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:10:00.8293351Z         
2025-03-14T05:10:00.8293671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:00.8293800Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:10:00.8293878Z         
2025-03-14T05:10:00.8294267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:00.8294474Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:10:00.8294548Z         
2025-03-14T05:10:00.8294959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:00.8295092Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:10:00.8295509Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:10:00.8295637Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:10:00.8295751Z         x_222: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:10:00.8295822Z         
2025-03-14T05:10:00.8296123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.8296314Z         tensor: "f32[269952, 4][4, 1]cpu" = x_218.to(torch.float32);  x_218 = None
2025-03-14T05:10:00.8296457Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_219.to(torch.float32);  x_219 = None
2025-03-14T05:10:00.8296588Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_220.to(torch.float32);  x_220 = None
2025-03-14T05:10:00.8296709Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_221.to(torch.float32);  x_221 = None
2025-03-14T05:10:00.8296838Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_222.to(torch.float32);  x_222 = None
2025-03-14T05:10:00.8296902Z         
2025-03-14T05:10:00.8297167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8297685Z         x_223: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_217, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_217 = None
2025-03-14T05:10:00.8297751Z         
2025-03-14T05:10:00.8298034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.8298233Z         x_224: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_223, inplace = False);  x_223 = None
2025-03-14T05:10:00.8298308Z         
2025-03-14T05:10:00.8298695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.8299213Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.8299293Z         
2025-03-14T05:10:00.8299654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.8300185Z         x_233: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_224, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_224 = None
2025-03-14T05:10:00.8300258Z         
2025-03-14T05:10:00.8300508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8301000Z         x_225: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_215, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_215 = None
2025-03-14T05:10:00.8301074Z         
2025-03-14T05:10:00.8301345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.8301545Z         x_226: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_225, inplace = False);  x_225 = None
2025-03-14T05:10:00.8301612Z         
2025-03-14T05:10:00.8301998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.8302522Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.8302595Z         
2025-03-14T05:10:00.8302979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.8306660Z         x_234: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_226, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_226 = None
2025-03-14T05:10:00.8306766Z         
2025-03-14T05:10:00.8307067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8307569Z         x_227: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_213, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_213 = None
2025-03-14T05:10:00.8307648Z         
2025-03-14T05:10:00.8307923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.8308121Z         x_228: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_227, inplace = False);  x_227 = None
2025-03-14T05:10:00.8308195Z         
2025-03-14T05:10:00.8308571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.8309091Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.8309213Z         
2025-03-14T05:10:00.8309582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.8310116Z         x_235: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_228, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_228 = None
2025-03-14T05:10:00.8310194Z         
2025-03-14T05:10:00.8310460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8310931Z         x_229: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_211, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_211 = None
2025-03-14T05:10:00.8311000Z         
2025-03-14T05:10:00.8311282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.8311465Z         x_230: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_229, inplace = False);  x_229 = None
2025-03-14T05:10:00.8311540Z         
2025-03-14T05:10:00.8311911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.8312416Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:00.8312504Z         
2025-03-14T05:10:00.8312920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.8313436Z         x_236: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_230, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_230 = None
2025-03-14T05:10:00.8313502Z         
2025-03-14T05:10:00.8313759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:00.8314531Z         x_231: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:10:00.8314608Z         
2025-03-14T05:10:00.8314894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:00.8315075Z         x_232: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_231, inplace = False);  x_231 = None
2025-03-14T05:10:00.8315140Z         
2025-03-14T05:10:00.8315518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:00.8316412Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:10:00.8316496Z         
2025-03-14T05:10:00.8316861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:00.8317693Z         x_237: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_232, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_232 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:10:00.8317770Z         
2025-03-14T05:10:00.8318111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:10:00.8318292Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:10:00.8318446Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:10:00.8318614Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:10:00.8318769Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:10:00.8318926Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:10:00.8319087Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:10:00.8319253Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:10:00.8319395Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:10:00.8319542Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:10:00.8319684Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:10:00.8319750Z         
2025-03-14T05:10:00.8320181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:10:00.8320365Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_233.view(4, -1, 4, 296, 304);  x_233 = None
2025-03-14T05:10:00.8320559Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:10:00.8320762Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:10:00.8320937Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_234.view(4, -1, 4, 148, 152);  x_234 = None
2025-03-14T05:10:00.8321112Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:10:00.8321292Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:10:00.8321443Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_235.view(4, -1, 4, 74, 76);  x_235 = None
2025-03-14T05:10:00.8321636Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:10:00.8321805Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:10:00.8321960Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_236.view(4, -1, 4, 37, 38);  x_236 = None
2025-03-14T05:10:00.8322137Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:10:00.8322316Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:10:00.8322456Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_237.view(4, -1, 4, 19, 19);  x_237 = None
2025-03-14T05:10:00.8322625Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:10:00.8322802Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:10:00.8322871Z         
2025-03-14T05:10:00.8323284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.8323492Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:10:00.8323566Z         
2025-03-14T05:10:00.8324001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.8324166Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:10:00.8324318Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:00.8324480Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:00.8324562Z         
2025-03-14T05:10:00.8324951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.8325125Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:00.8325198Z         
2025-03-14T05:10:00.8325515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.8325669Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:00.8325735Z         
2025-03-14T05:10:00.8326060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.8326194Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:00.8326330Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8326488Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:00.8326561Z         
2025-03-14T05:10:00.8326878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.8327016Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:00.8327140Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:00.8327329Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:10:00.8327394Z         
2025-03-14T05:10:00.8327716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.8327842Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8327939Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:00.8328098Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:10:00.8328173Z         
2025-03-14T05:10:00.8328489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.8328648Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:00.8328739Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:00.8328880Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:10:00.8328945Z         
2025-03-14T05:10:00.8329292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.8329450Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.8329573Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:10:00.8329637Z         
2025-03-14T05:10:00.8329948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.8330109Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.8330223Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:10:00.8330297Z         
2025-03-14T05:10:00.8330610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.8330785Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.8330896Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:10:00.8330970Z         
2025-03-14T05:10:00.8331271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.8331462Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:00.8331573Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:10:00.8331647Z         
2025-03-14T05:10:00.8331984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.8332136Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:00.8332200Z         
2025-03-14T05:10:00.8332543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.8332680Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:00.8332751Z         
2025-03-14T05:10:00.8333092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.8333241Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:00.8333387Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:10:00.8333551Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:00.8333694Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:10:00.8333768Z         
2025-03-14T05:10:00.8334147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.8334298Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:00.8334422Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:10:00.8334583Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:00.8334723Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:10:00.8334799Z         
2025-03-14T05:10:00.8335130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.8335259Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:00.8335422Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:00.8335562Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:10:00.8335626Z         
2025-03-14T05:10:00.8335968Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.8336092Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:00.8336276Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:00.8336432Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:10:00.8336497Z         
2025-03-14T05:10:00.8336816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.8336916Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:00.8337046Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:00.8337111Z         
2025-03-14T05:10:00.8337422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.8337516Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:00.8337641Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:00.8337707Z         
2025-03-14T05:10:00.8338034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.8338155Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:00.8338290Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:00.8338352Z         
2025-03-14T05:10:00.8338653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.8338763Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:00.8338892Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:00.8338971Z         
2025-03-14T05:10:00.8339324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.8339508Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:00.8339580Z         
2025-03-14T05:10:00.8339930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.8340103Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:00.8340169Z         
2025-03-14T05:10:00.8340558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.8340738Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:00.8340810Z         
2025-03-14T05:10:00.8341203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.8341420Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:00.8341484Z         
2025-03-14T05:10:00.8341916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.8342071Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:10:00.8342229Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:00.8342385Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:00.8342473Z         
2025-03-14T05:10:00.8342844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.8343020Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:00.8343084Z         
2025-03-14T05:10:00.8343400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.8343545Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:00.8343616Z         
2025-03-14T05:10:00.8343926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.8344068Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:00.8344291Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8344453Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:10:00.8344528Z         
2025-03-14T05:10:00.8344851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.8344988Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:00.8345125Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:00.8345308Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:10:00.8345373Z         
2025-03-14T05:10:00.8345694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.8345820Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8345923Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:00.8346075Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:10:00.8346148Z         
2025-03-14T05:10:00.8346468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.8346622Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:00.8346717Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:00.8346850Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:10:00.8346917Z         
2025-03-14T05:10:00.8347218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.8347368Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.8347492Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:10:00.8347555Z         
2025-03-14T05:10:00.8347850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.8347997Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.8348117Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:10:00.8348180Z         
2025-03-14T05:10:00.8348495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.8348658Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.8348777Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:10:00.8348841Z         
2025-03-14T05:10:00.8349141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.8349323Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:00.8349441Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:10:00.8349508Z         
2025-03-14T05:10:00.8349842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.8349981Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:00.8350052Z         
2025-03-14T05:10:00.8350373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.8350513Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:00.8350584Z         
2025-03-14T05:10:00.8350914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.8351083Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.8351210Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:10:00.8351369Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:00.8351510Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:10:00.8351579Z         
2025-03-14T05:10:00.8351930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.8352074Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:00.8352195Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:10:00.8352352Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:00.8352491Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:10:00.8352561Z         
2025-03-14T05:10:00.8352882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.8353002Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:00.8353159Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:00.8353298Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:10:00.8353361Z         
2025-03-14T05:10:00.8353688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.8353802Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:00.8353989Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:00.8354139Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:10:00.8354210Z         
2025-03-14T05:10:00.8354510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.8354616Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:00.8354732Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:00.8354804Z         
2025-03-14T05:10:00.8355097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.8355197Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:00.8355312Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:00.8355384Z         
2025-03-14T05:10:00.8355676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.8355798Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:00.8355930Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:00.8356377Z         
2025-03-14T05:10:00.8356697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.8356863Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:00.8357043Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:00.8357145Z         
2025-03-14T05:10:00.8357530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.8357805Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:00.8357933Z         
2025-03-14T05:10:00.8358311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.8358493Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:00.8358608Z         
2025-03-14T05:10:00.8359013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.8359242Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:00.8359328Z         
2025-03-14T05:10:00.8359771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.8359984Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:00.8360121Z         
2025-03-14T05:10:00.8360567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.8360762Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:10:00.8360974Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:00.8361135Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:00.8361280Z         
2025-03-14T05:10:00.8361669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.8361881Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:10:00.8361967Z         
2025-03-14T05:10:00.8362306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.8362485Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:00.8362635Z         
2025-03-14T05:10:00.8362962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.8363145Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:00.8363290Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8363497Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:10:00.8363590Z         
2025-03-14T05:10:00.8363941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.8364083Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:00.8364266Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:00.8364430Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:10:00.8364581Z         
2025-03-14T05:10:00.8364904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.8365086Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8365200Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:00.8365375Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:10:00.8365476Z         
2025-03-14T05:10:00.8365838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.8366006Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:00.8366136Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:00.8421054Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:10:00.8421280Z         
2025-03-14T05:10:00.8421779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.8422003Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.8422138Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:10:00.8422222Z         
2025-03-14T05:10:00.8422623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.8422817Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.8423154Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:10:00.8423293Z         
2025-03-14T05:10:00.8423695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.8423853Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.8423978Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:10:00.8424047Z         
2025-03-14T05:10:00.8424502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.8424698Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:00.8424824Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:10:00.8424891Z         
2025-03-14T05:10:00.8425252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.8425401Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:00.8425476Z         
2025-03-14T05:10:00.8425852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.8425998Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:00.8426074Z         
2025-03-14T05:10:00.8426472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.8426664Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.8426797Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:10:00.8426966Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:00.8427159Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:10:00.8427230Z         
2025-03-14T05:10:00.8427620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.8427769Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:00.8427896Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:10:00.8428063Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:00.8428206Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:10:00.8428278Z         
2025-03-14T05:10:00.8428654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.8428783Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:00.8428951Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:00.8429104Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:10:00.8429169Z         
2025-03-14T05:10:00.8429555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.8429695Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:00.8429892Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:00.8430028Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:10:00.8430101Z         
2025-03-14T05:10:00.8430437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.8430547Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:00.8430735Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:00.8430813Z         
2025-03-14T05:10:00.8431156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.8431267Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:00.8431386Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:00.8431457Z         
2025-03-14T05:10:00.8431783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.8431906Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:00.8432041Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:00.8432116Z         
2025-03-14T05:10:00.8432417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.8432557Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:00.8432689Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:00.8432763Z         
2025-03-14T05:10:00.8433111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.8433331Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:00.8433395Z         
2025-03-14T05:10:00.8433738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.8433902Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:00.8433974Z         
2025-03-14T05:10:00.8434358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.8434547Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:00.8434619Z         
2025-03-14T05:10:00.8435020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.8435236Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:10:00.8435301Z         
2025-03-14T05:10:00.8435738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.8435908Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:10:00.8436085Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:00.8436223Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:00.8436294Z         
2025-03-14T05:10:00.8436664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.8436836Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:10:00.8436899Z         
2025-03-14T05:10:00.8437213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.8437359Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:00.8437431Z         
2025-03-14T05:10:00.8437744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.8437880Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:00.8438007Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8438164Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:10:00.8438228Z         
2025-03-14T05:10:00.8438548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.8438671Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:00.8438820Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:00.8438974Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:10:00.8439044Z         
2025-03-14T05:10:00.8439354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.8439503Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8439596Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:00.8439734Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:10:00.8439797Z         
2025-03-14T05:10:00.8440115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.8440263Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:00.8440369Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:00.8440497Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:10:00.8440569Z         
2025-03-14T05:10:00.8440876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.8441036Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.8441147Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:10:00.8441219Z         
2025-03-14T05:10:00.8441523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.8441673Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.8441806Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:10:00.8441884Z         
2025-03-14T05:10:00.8442185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.8442336Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.8442451Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:10:00.8442513Z         
2025-03-14T05:10:00.8442817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.8443000Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:00.8443117Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:10:00.8443181Z         
2025-03-14T05:10:00.8443519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.8443659Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:00.8443728Z         
2025-03-14T05:10:00.8444054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.8444190Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:00.8444253Z         
2025-03-14T05:10:00.8446069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.8446315Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.8446455Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:10:00.8446613Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:00.8446783Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:10:00.8446853Z         
2025-03-14T05:10:00.8447209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.8447352Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:00.8447479Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:10:00.8447628Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:00.8447771Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:10:00.8447835Z         
2025-03-14T05:10:00.8448161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.8448276Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:00.8448439Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:00.8448576Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:10:00.8448640Z         
2025-03-14T05:10:00.8448991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.8449683Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:00.8449863Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:00.8449999Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:10:00.8450073Z         
2025-03-14T05:10:00.8450385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.8450486Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:00.8450603Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:00.8450673Z         
2025-03-14T05:10:00.8450974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.8451076Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:00.8451190Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:00.8451259Z         
2025-03-14T05:10:00.8451555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.8451674Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:00.8451806Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:00.8451876Z         
2025-03-14T05:10:00.8452174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.8452315Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:00.8452443Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:00.8452513Z         
2025-03-14T05:10:00.8452854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.8453064Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:00.8453130Z         
2025-03-14T05:10:00.8453469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.8453632Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:00.8453705Z         
2025-03-14T05:10:00.8454084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.8454266Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:00.8454330Z         
2025-03-14T05:10:00.8454735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:00.8454938Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:10:00.8455007Z         
2025-03-14T05:10:00.8455429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:00.8455602Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:10:00.8455767Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:00.8455906Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:00.8455975Z         
2025-03-14T05:10:00.8456353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:00.8456515Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:10:00.8456584Z         
2025-03-14T05:10:00.8456883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:00.8457031Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:00.8457094Z         
2025-03-14T05:10:00.8457398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:00.8457529Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:00.8457649Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8457798Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:10:00.8457859Z         
2025-03-14T05:10:00.8458269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:00.8458423Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:00.8458546Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:00.8458703Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:10:00.8458770Z         
2025-03-14T05:10:00.8459086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:00.8459208Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:00.8459296Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:00.8459425Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:10:00.8459487Z         
2025-03-14T05:10:00.8459794Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:00.8459940Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:00.8460033Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:00.8460153Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:10:00.8460218Z         
2025-03-14T05:10:00.8460521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:00.8460678Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:00.8460790Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:10:00.8460861Z         
2025-03-14T05:10:00.8461152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:00.8461321Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:00.8461450Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:10:00.8461522Z         
2025-03-14T05:10:00.8461809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:00.8461962Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:00.8462068Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:10:00.8462143Z         
2025-03-14T05:10:00.8462434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:00.8462624Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:00.8462731Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:10:00.8462802Z         
2025-03-14T05:10:00.8463129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:00.8463276Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:00.8463340Z         
2025-03-14T05:10:00.8463673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:00.8463804Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:00.8463877Z         
2025-03-14T05:10:00.8464296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:00.8464449Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.8464580Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:10:00.8464752Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:00.8464901Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:10:00.8464969Z         
2025-03-14T05:10:00.8465330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:00.8465467Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:00.8465599Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:10:00.8465754Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:00.8465902Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:10:00.8465969Z         
2025-03-14T05:10:00.8466310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:00.8466430Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:00.8466609Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:00.8466741Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:10:00.8466815Z         
2025-03-14T05:10:00.8467158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:00.8467310Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:00.8467475Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:00.8467613Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:10:00.8467678Z         
2025-03-14T05:10:00.8467988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:00.8468088Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:00.8468210Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:00.8468277Z         
2025-03-14T05:10:00.8468591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:00.8468685Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:00.8468803Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:00.8468867Z         
2025-03-14T05:10:00.8469177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:00.8469290Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:00.8469430Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:00.8469494Z         
2025-03-14T05:10:00.8469799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:00.8469933Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:00.8470072Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:00.8470137Z         
2025-03-14T05:10:00.8470514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:00.8470702Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:00.8470776Z         
2025-03-14T05:10:00.8471102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:00.8471268Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:00.8471335Z         
2025-03-14T05:10:00.8471716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:00.8471887Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:00.8471959Z         
2025-03-14T05:10:00.8472444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:00.8472579Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:00.8472650Z         
2025-03-14T05:10:00.8472943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8473113Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:10:00.8473195Z         
2025-03-14T05:10:00.8473633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.8473748Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:10:00.8473858Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:00.8473972Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:00.8474046Z         
2025-03-14T05:10:00.8474505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.8474647Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.8474880Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:10:00.8474951Z         
2025-03-14T05:10:00.8475403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.8475578Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.8475643Z         
2025-03-14T05:10:00.8475948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8476086Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:00.8476162Z         
2025-03-14T05:10:00.8476592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.8476719Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:10:00.8476845Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:00.8476975Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:00.8477039Z         
2025-03-14T05:10:00.8477500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.8477632Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.8477875Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:10:00.8477941Z         
2025-03-14T05:10:00.8478395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.8478568Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.8478633Z         
2025-03-14T05:10:00.8478932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8479057Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:00.8479132Z         
2025-03-14T05:10:00.8479575Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.8479717Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:10:00.8479822Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:00.8479948Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:00.8480013Z         
2025-03-14T05:10:00.8480469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.8480603Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.8480845Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:10:00.8480911Z         
2025-03-14T05:10:00.8481368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.8481810Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.8481889Z         
2025-03-14T05:10:00.8482176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8482306Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:00.8482371Z         
2025-03-14T05:10:00.8482791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.8482963Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:10:00.8483072Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:00.8483186Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:00.8483290Z         
2025-03-14T05:10:00.8483727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.8483864Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:00.8484092Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:10:00.8484179Z         
2025-03-14T05:10:00.8484621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.8484779Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.8484852Z         
2025-03-14T05:10:00.8485134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8485262Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:00.8485324Z         
2025-03-14T05:10:00.8485742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:00.8485877Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:10:00.8486011Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:00.8486124Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:00.8486196Z         
2025-03-14T05:10:00.8486628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:00.8486795Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:00.8487018Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:10:00.8487091Z         
2025-03-14T05:10:00.8487521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:00.8487689Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:00.8487752Z         
2025-03-14T05:10:00.8488040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:00.8488160Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:00.8488230Z         
2025-03-14T05:10:00.8488496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.8488890Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:00.8488955Z         
2025-03-14T05:10:00.8489235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.8489705Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:00.8489770Z         
2025-03-14T05:10:00.8490041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:00.8490233Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:10:00.8490307Z         
2025-03-14T05:10:00.8490675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:00.8490820Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:00.8490887Z         
2025-03-14T05:10:00.8491182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:00.8491324Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:10:00.8491393Z         
2025-03-14T05:10:00.8491753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:00.8491917Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:00.8492000Z         
2025-03-14T05:10:00.8492478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:00.8492617Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:10:00.8492747Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:00.8492902Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:00.8493043Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:00.8493108Z         
2025-03-14T05:10:00.8493478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:00.8493597Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:00.8493672Z         
2025-03-14T05:10:12.1201377Z 
2025-03-14T05:10:12.1206077Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:12.1210619Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:10:12.1213283Z         l_features_p2_ = L_features_p2_
2025-03-14T05:10:12.1217575Z         l_features_p3_ = L_features_p3_
2025-03-14T05:10:12.1218009Z         l_features_p4_ = L_features_p4_
2025-03-14T05:10:12.1218281Z         l_features_p5_ = L_features_p5_
2025-03-14T05:10:12.1218632Z         l_features_p6_ = L_features_p6_
2025-03-14T05:10:12.1219696Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:10:12.1220454Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:10:12.1221046Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:10:12.1221670Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:10:12.1222240Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:10:12.1222782Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:10:12.1223495Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:10:12.1224098Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:10:12.1224826Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:10:12.1225388Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:10:12.1225927Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:10:12.1226292Z         
2025-03-14T05:10:12.1226884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1227566Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:10:12.1227839Z         
2025-03-14T05:10:12.1228235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1228740Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:12.1229003Z         
2025-03-14T05:10:12.1229528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1230164Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:10:12.1230475Z         
2025-03-14T05:10:12.1230862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1231364Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:10:12.1231628Z         
2025-03-14T05:10:12.1232121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1232729Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:10:12.1233068Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:10:12.1233343Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:10:12.1233581Z         
2025-03-14T05:10:12.1233995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1234510Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:10:12.1234761Z         
2025-03-14T05:10:12.1235182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1235685Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:10:12.1235929Z         
2025-03-14T05:10:12.1236396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1237041Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:10:12.1237373Z         
2025-03-14T05:10:12.1237903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1238531Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:10:12.1239034Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:10:12.1239525Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:10:12.1239858Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:10:12.1240092Z         
2025-03-14T05:10:12.1240617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1241263Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:10:12.1241541Z         
2025-03-14T05:10:12.1241927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1242431Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:10:12.1242697Z         
2025-03-14T05:10:12.1243215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1243841Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:10:12.1244133Z         
2025-03-14T05:10:12.1244523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1244999Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:10:12.1245258Z         
2025-03-14T05:10:12.1245724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1246327Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:10:12.1246674Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:10:12.1246952Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:10:12.1247194Z         
2025-03-14T05:10:12.1247599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1248104Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:10:12.1248348Z         
2025-03-14T05:10:12.1248751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1249250Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:10:12.1249495Z         
2025-03-14T05:10:12.1249947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1250573Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:10:12.1250902Z         
2025-03-14T05:10:12.1251415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1252035Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:10:12.1252539Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:10:12.1253036Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:10:12.1253344Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:10:12.1253587Z         
2025-03-14T05:10:12.1254114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1254756Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:10:12.1255031Z         
2025-03-14T05:10:12.1255418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1255910Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:10:12.1256175Z         
2025-03-14T05:10:12.1256689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1257318Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:10:12.1257599Z         
2025-03-14T05:10:12.1257969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1258438Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:10:12.1258692Z         
2025-03-14T05:10:12.1259151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1259757Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:10:12.1260105Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:10:12.1260374Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:10:12.1260613Z         
2025-03-14T05:10:12.1261022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1261526Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:10:12.1261769Z         
2025-03-14T05:10:12.1262166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1262673Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:10:12.1262918Z         
2025-03-14T05:10:12.1263389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1264037Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:10:12.1264472Z         
2025-03-14T05:10:12.1264989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1265605Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:10:12.1266109Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:10:12.1266601Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:10:12.1266896Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:10:12.1267129Z         
2025-03-14T05:10:12.1267641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1268261Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:10:12.1268522Z         
2025-03-14T05:10:12.1268893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1269376Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:10:12.1269633Z         
2025-03-14T05:10:12.1270144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1270777Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:10:12.1271079Z         
2025-03-14T05:10:12.1271450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1271929Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:10:12.1272180Z         
2025-03-14T05:10:12.1272648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1273250Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:10:12.1273593Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:10:12.1273861Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:10:12.1274099Z         
2025-03-14T05:10:12.1274503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1275001Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:10:12.1275246Z         
2025-03-14T05:10:12.1275647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1276141Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:10:12.1276383Z         
2025-03-14T05:10:12.1276833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1277452Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:10:12.1277775Z         
2025-03-14T05:10:12.1278272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1278867Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:10:12.1279356Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:10:12.1279840Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:10:12.1280131Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:10:12.1280366Z         
2025-03-14T05:10:12.1280871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1281729Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:12.1282009Z         
2025-03-14T05:10:12.1282392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1282876Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:10:12.1283141Z         
2025-03-14T05:10:12.1283653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1284278Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:12.1284602Z         
2025-03-14T05:10:12.1284975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1285453Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:10:12.1285709Z         
2025-03-14T05:10:12.1288224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1289140Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:10:12.1289738Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:10:12.1290030Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:10:12.1290484Z         
2025-03-14T05:10:12.1290913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1291604Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:10:12.1292021Z         
2025-03-14T05:10:12.1292427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1293118Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:10:12.1293546Z         
2025-03-14T05:10:12.1294027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1294847Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:10:12.1295185Z         
2025-03-14T05:10:12.1295783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1298469Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:10:12.1298982Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:10:12.1299474Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:10:12.1299780Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:10:12.1300022Z         
2025-03-14T05:10:12.1300419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:12.1300904Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:10:12.1301218Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:10:12.1301529Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:10:12.1301830Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:10:12.1302134Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:10:12.1302377Z         
2025-03-14T05:10:12.1302733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1303485Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:10:12.1304074Z         
2025-03-14T05:10:12.1304560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1305104Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:10:12.1305432Z         
2025-03-14T05:10:12.1306074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1306930Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1307459Z         
2025-03-14T05:10:12.1307918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1308756Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:10:12.1309284Z         
2025-03-14T05:10:12.1309631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1310351Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:10:12.1310879Z         
2025-03-14T05:10:12.1311231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1311754Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:10:12.1312077Z         
2025-03-14T05:10:12.1312537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1313357Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1313869Z         
2025-03-14T05:10:12.1314314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1315121Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:10:12.1315632Z         
2025-03-14T05:10:12.1315973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1316669Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:10:12.1317171Z         
2025-03-14T05:10:12.1317528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1318038Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:10:12.1318331Z         
2025-03-14T05:10:12.1318783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1319598Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1320097Z         
2025-03-14T05:10:12.1320556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1321345Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:10:12.1321850Z         
2025-03-14T05:10:12.1322177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1322864Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:10:12.1323359Z         
2025-03-14T05:10:12.1323711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1324204Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:10:12.1324504Z         
2025-03-14T05:10:12.1325024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1325884Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1326378Z         
2025-03-14T05:10:12.1326812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1327584Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:10:12.1328076Z         
2025-03-14T05:10:12.1328408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1329266Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:10:12.1329934Z         
2025-03-14T05:10:12.1330282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1330764Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:10:12.1331066Z         
2025-03-14T05:10:12.1331517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1332556Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:10:12.1333279Z         
2025-03-14T05:10:12.1333711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1334696Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:10:12.1335388Z         
2025-03-14T05:10:12.1335810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:10:12.1336355Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:10:12.1336709Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:10:12.1337072Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:10:12.1337434Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:10:12.1337797Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:10:12.1338158Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:10:12.1338502Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:10:12.1338846Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:10:12.1339187Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:10:12.1339518Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:10:12.1339778Z         
2025-03-14T05:10:12.1340284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:10:12.1340917Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:10:12.1341329Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:10:12.1341737Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:10:12.1342129Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:10:12.1342515Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:10:12.1342914Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:10:12.1343294Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:10:12.1343655Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:10:12.1344051Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:10:12.1344551Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:10:12.1344919Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:10:12.1345339Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:10:12.1345698Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:10:12.1346059Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:10:12.1346444Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:10:12.1346734Z         
2025-03-14T05:10:12.1347242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1347911Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:10:12.1348238Z         
2025-03-14T05:10:12.1348763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1349409Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:10:12.1349786Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:12.1350146Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:12.1350410Z         
2025-03-14T05:10:12.1350874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1351474Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:12.1351762Z         
2025-03-14T05:10:12.1352175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1352689Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:12.1352955Z         
2025-03-14T05:10:12.1353359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1353872Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1354190Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1354521Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:12.1354794Z         
2025-03-14T05:10:12.1355206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1355705Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1356012Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1356366Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:10:12.1356644Z         
2025-03-14T05:10:12.1357052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1357555Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1357850Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:12.1358119Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:10:12.1358361Z         
2025-03-14T05:10:12.1358751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1359250Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:12.1359541Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:12.1359813Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:10:12.1360063Z         
2025-03-14T05:10:12.1360469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1360972Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1361297Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:10:12.1361531Z         
2025-03-14T05:10:12.1361915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1362412Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1362736Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:10:12.1362973Z         
2025-03-14T05:10:12.1363368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1363887Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1364207Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:10:12.1364440Z         
2025-03-14T05:10:12.1364820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1365343Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:12.1365685Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:10:12.1365919Z         
2025-03-14T05:10:12.1366336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1366859Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:12.1367116Z         
2025-03-14T05:10:12.1367527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1368040Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:12.1368294Z         
2025-03-14T05:10:12.1368712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1369260Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:12.1369582Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:10:12.1369922Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:12.1370269Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:10:12.1370534Z         
2025-03-14T05:10:12.1370984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1371514Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:12.1371828Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:10:12.1372157Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:12.1372503Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:10:12.1372753Z         
2025-03-14T05:10:12.1373165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1373663Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:12.1373989Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:12.1374332Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:10:12.1374588Z         
2025-03-14T05:10:12.1375005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1375500Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:12.1375859Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:12.1376229Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:10:12.1376491Z         
2025-03-14T05:10:12.1376893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1377357Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:12.1377633Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:12.1377871Z         
2025-03-14T05:10:12.1378264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1378724Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:12.1378994Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:12.1379234Z         
2025-03-14T05:10:12.1379621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1380095Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:12.1380397Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:12.1380655Z         
2025-03-14T05:10:12.1381041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1381719Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:12.1382019Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:12.1382313Z         
2025-03-14T05:10:12.1382743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1383323Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:12.1383625Z         
2025-03-14T05:10:12.1384079Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1385490Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:12.1385795Z         
2025-03-14T05:10:12.1386276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1386901Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:12.1387228Z         
2025-03-14T05:10:12.1387713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1388376Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:12.1388708Z         
2025-03-14T05:10:12.1389225Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1389866Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:10:12.1390280Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:12.1390662Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:12.1390931Z         
2025-03-14T05:10:12.1391399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1391996Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:12.1392290Z         
2025-03-14T05:10:12.1392704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1393216Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:12.1393478Z         
2025-03-14T05:10:12.1393878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1394378Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1394698Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1395042Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:10:12.1395318Z         
2025-03-14T05:10:12.1395730Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1396240Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1396552Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1396913Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:10:12.1397198Z         
2025-03-14T05:10:12.1397597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1398090Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1398392Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:12.1398680Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:10:12.1398940Z         
2025-03-14T05:10:12.1399343Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1399868Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:12.1400170Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:12.1400446Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:10:12.1400698Z         
2025-03-14T05:10:12.1401099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1401601Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1401927Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:10:12.1402166Z         
2025-03-14T05:10:12.1402550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1403055Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1403388Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:10:12.1403652Z         
2025-03-14T05:10:12.1404058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1404566Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1404895Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:10:12.1405137Z         
2025-03-14T05:10:12.1405528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1406071Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:12.1406426Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:10:12.1406665Z         
2025-03-14T05:10:12.1407091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1407636Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:12.1407894Z         
2025-03-14T05:10:12.1408308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1408825Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:12.1409080Z         
2025-03-14T05:10:12.1409503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1410051Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:12.1410375Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:10:12.1410717Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:12.1411097Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:10:12.1411362Z         
2025-03-14T05:10:12.1411791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1412344Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:12.1412669Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:10:12.1413002Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:12.1413359Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:10:12.1413621Z         
2025-03-14T05:10:12.1414042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1414547Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:12.1414884Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:12.1415246Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:10:12.1415515Z         
2025-03-14T05:10:12.1415948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1416469Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:12.1416826Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:12.1417188Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:10:12.1417448Z         
2025-03-14T05:10:12.1417848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1418310Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:12.1418595Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:12.1418842Z         
2025-03-14T05:10:12.1419237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1419695Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:12.1419965Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:12.1420206Z         
2025-03-14T05:10:12.1420603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1421082Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:12.1421394Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:12.1421650Z         
2025-03-14T05:10:12.1422039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1422516Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:12.1422847Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:12.1423102Z         
2025-03-14T05:10:12.1423538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1424247Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:12.1424579Z         
2025-03-14T05:10:12.1425031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1425591Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:12.1425887Z         
2025-03-14T05:10:12.1426367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1426981Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:12.1427284Z         
2025-03-14T05:10:12.1427770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1428430Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:12.1428759Z         
2025-03-14T05:10:12.1429276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1429938Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:10:12.1430322Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:12.1430672Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:12.1430929Z         
2025-03-14T05:10:12.1431390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1431982Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:10:12.1432272Z         
2025-03-14T05:10:12.1432671Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1433179Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:12.1433449Z         
2025-03-14T05:10:12.1433844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1434347Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1434658Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1434992Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:10:12.1435265Z         
2025-03-14T05:10:12.1435677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1436174Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1436501Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1436835Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:10:12.1437107Z         
2025-03-14T05:10:12.1437502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1438002Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1438278Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:12.1438559Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:10:12.1438818Z         
2025-03-14T05:10:12.1439219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1439740Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:12.1440044Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:12.1440325Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:10:12.1440574Z         
2025-03-14T05:10:12.1440972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1441490Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1441822Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:10:12.1442063Z         
2025-03-14T05:10:12.1442455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1442967Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1443316Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:10:12.1443576Z         
2025-03-14T05:10:12.1443963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1444470Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1444788Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:10:12.1445025Z         
2025-03-14T05:10:12.1445413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1445952Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:12.1446305Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:10:12.1446537Z         
2025-03-14T05:10:12.1446958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1447489Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:12.1447754Z         
2025-03-14T05:10:12.1448174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1448701Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:12.1448963Z         
2025-03-14T05:10:12.1449395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1449950Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:12.1450271Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:10:12.1450611Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:12.1450981Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:10:12.1451250Z         
2025-03-14T05:10:12.1451693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1452234Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:12.1452557Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:10:12.1452885Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:12.1453232Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:10:12.1453495Z         
2025-03-14T05:10:12.1453925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1454441Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:12.1454775Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:12.1455130Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:10:12.1455391Z         
2025-03-14T05:10:12.1455841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1456361Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:12.1456696Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:12.1457055Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:10:12.1457317Z         
2025-03-14T05:10:12.1457716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1458178Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:12.1458450Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:12.1458690Z         
2025-03-14T05:10:12.1459089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1459549Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:12.1459812Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:12.1460048Z         
2025-03-14T05:10:12.1460439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1460920Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:12.1461224Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:12.1461479Z         
2025-03-14T05:10:12.1461866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1462358Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:12.1462662Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:12.1462918Z         
2025-03-14T05:10:12.1463353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1463956Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:12.1464351Z         
2025-03-14T05:10:12.1464777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1465334Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:12.1465631Z         
2025-03-14T05:10:12.1466101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1466709Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:12.1467007Z         
2025-03-14T05:10:12.1467492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1468150Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:10:12.1468479Z         
2025-03-14T05:10:12.1468996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1469653Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:10:12.1470024Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:12.1470363Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:12.1470623Z         
2025-03-14T05:10:12.1471083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1471651Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:10:12.1471930Z         
2025-03-14T05:10:12.1472312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1472810Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:12.1473073Z         
2025-03-14T05:10:12.1473453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1473934Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1474235Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1474558Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:10:12.1474825Z         
2025-03-14T05:10:12.1475221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1475732Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1476029Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1476355Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:10:12.1476620Z         
2025-03-14T05:10:12.1477020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1477495Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1477757Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:12.1478024Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:10:12.1478269Z         
2025-03-14T05:10:12.1478655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1479159Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:12.1479451Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:12.1479718Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:10:12.1479962Z         
2025-03-14T05:10:12.1480348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1480838Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1481158Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:10:12.1481390Z         
2025-03-14T05:10:12.1481940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1482477Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1482826Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:10:12.1483062Z         
2025-03-14T05:10:12.1483440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1483933Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1484252Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:10:12.1484486Z         
2025-03-14T05:10:12.1484865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1485392Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:12.1485734Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:10:12.1485965Z         
2025-03-14T05:10:12.1486379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1486901Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:12.1487157Z         
2025-03-14T05:10:12.1487566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1488076Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:12.1488357Z         
2025-03-14T05:10:12.1488785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1489322Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:12.1489643Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:10:12.1490007Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:12.1490350Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:10:12.1490607Z         
2025-03-14T05:10:12.1491030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1491557Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:12.1491870Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:10:12.1492187Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:12.1492523Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:10:12.1492777Z         
2025-03-14T05:10:12.1493188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1493673Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:12.1494000Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:12.1494342Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:10:12.1494595Z         
2025-03-14T05:10:12.1495021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1495521Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:12.1495845Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:12.1496189Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:10:12.1496441Z         
2025-03-14T05:10:12.1496828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1497277Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:12.1497540Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:12.1497772Z         
2025-03-14T05:10:12.1498157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1498606Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:12.1498869Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:12.1499101Z         
2025-03-14T05:10:12.1499490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1499957Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:12.1500255Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:12.1500508Z         
2025-03-14T05:10:12.1500886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1501366Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:12.1501665Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:12.1501914Z         
2025-03-14T05:10:12.1502386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1502961Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:12.1503258Z         
2025-03-14T05:10:12.1503661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1504273Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:12.1504575Z         
2025-03-14T05:10:12.1505073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1505692Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:12.1505984Z         
2025-03-14T05:10:12.1506458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1507114Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:10:12.1507442Z         
2025-03-14T05:10:12.1507987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1508640Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:10:12.1509001Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:12.1509352Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:12.1509615Z         
2025-03-14T05:10:12.1510077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1510666Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:10:12.1510955Z         
2025-03-14T05:10:12.1511356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1511868Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:12.1512126Z         
2025-03-14T05:10:12.1512527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1513033Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1513340Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1513669Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:10:12.1513939Z         
2025-03-14T05:10:12.1514348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1514868Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1515174Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1515506Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:10:12.1515779Z         
2025-03-14T05:10:12.1516196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1516683Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1516950Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:12.1517230Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:10:12.1517482Z         
2025-03-14T05:10:12.1517884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1518395Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:12.1518695Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:12.1518973Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:10:12.1519217Z         
2025-03-14T05:10:12.1519606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1520099Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1520415Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:10:12.1520646Z         
2025-03-14T05:10:12.1521017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1521524Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1521866Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:10:12.1522095Z         
2025-03-14T05:10:12.1522480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1522969Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1523280Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:10:12.1523508Z         
2025-03-14T05:10:12.1523887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1524411Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:12.1524745Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:10:12.1524974Z         
2025-03-14T05:10:12.1525386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1525895Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:12.1526145Z         
2025-03-14T05:10:12.1526552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1527061Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:12.1527328Z         
2025-03-14T05:10:12.1527749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1528271Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:12.1528582Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:10:12.1528924Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:12.1529269Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:10:12.1529516Z         
2025-03-14T05:10:12.1529944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1530469Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:12.1530778Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:10:12.1531099Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:12.1531438Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:10:12.1531692Z         
2025-03-14T05:10:12.1532108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1532601Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:12.1532919Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:12.1533262Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:10:12.1533514Z         
2025-03-14T05:10:12.1533944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1534448Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:12.1534768Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:12.1535109Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:10:12.1535353Z         
2025-03-14T05:10:12.1535743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1536195Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:12.1536452Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:12.1536684Z         
2025-03-14T05:10:12.1537068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1537512Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:12.1537771Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:12.1538007Z         
2025-03-14T05:10:12.1538393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1538861Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:12.1539159Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:12.1539409Z         
2025-03-14T05:10:12.1539791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1540271Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:12.1540565Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:12.1540809Z         
2025-03-14T05:10:12.1541244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1541817Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:12.1542113Z         
2025-03-14T05:10:12.1542519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1543051Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:12.1543327Z         
2025-03-14T05:10:12.1543781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1547181Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:12.1547507Z         
2025-03-14T05:10:12.1548106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:12.1548830Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:12.1549093Z         
2025-03-14T05:10:12.1549492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1550041Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:10:12.1550332Z         
2025-03-14T05:10:12.1550869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1551485Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:10:12.1551760Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:12.1552037Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:12.1552282Z         
2025-03-14T05:10:12.1552835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1553490Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1553919Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:10:12.1554267Z         
2025-03-14T05:10:12.1554815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1555497Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1555785Z         
2025-03-14T05:10:12.1556175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1556673Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:12.1556919Z         
2025-03-14T05:10:12.1557439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1558055Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:10:12.1558344Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:12.1558621Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:12.1558858Z         
2025-03-14T05:10:12.1559401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1560033Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1560452Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:10:12.1560796Z         
2025-03-14T05:10:12.1561335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1561992Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1562274Z         
2025-03-14T05:10:12.1562650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1563118Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:12.1563359Z         
2025-03-14T05:10:12.1563892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1564491Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:10:12.1564768Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:12.1565040Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:12.1565277Z         
2025-03-14T05:10:12.1565805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1566435Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1566860Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:10:12.1567207Z         
2025-03-14T05:10:12.1567735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1568387Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1568668Z         
2025-03-14T05:10:12.1569042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1569512Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:12.1569778Z         
2025-03-14T05:10:12.1570283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1570861Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:10:12.1571132Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:12.1571420Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:12.1571656Z         
2025-03-14T05:10:12.1572191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1572812Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1573233Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:10:12.1573582Z         
2025-03-14T05:10:12.1574107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1574763Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1575039Z         
2025-03-14T05:10:12.1575410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1575877Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:12.1576120Z         
2025-03-14T05:10:12.1576657Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1577261Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:10:12.1577530Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:12.1577802Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:12.1578040Z         
2025-03-14T05:10:12.1578569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1579225Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:12.1579671Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:10:12.1580023Z         
2025-03-14T05:10:12.1580548Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1581200Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1581671Z         
2025-03-14T05:10:12.1582054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1582518Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:12.1582748Z         
2025-03-14T05:10:12.1583105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1583866Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:12.1584409Z         
2025-03-14T05:10:12.1584770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1585643Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:12.1586215Z         
2025-03-14T05:10:12.1586574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1587109Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:10:12.1587433Z         
2025-03-14T05:10:12.1587894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:12.1588490Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:12.1588754Z         
2025-03-14T05:10:12.1589138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:12.1589639Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:10:12.1589912Z         
2025-03-14T05:10:12.1590405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:12.1590996Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:12.1591252Z         
2025-03-14T05:10:12.1591822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:12.1592486Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:10:12.1592804Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:12.1593144Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:12.1593485Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:12.1593745Z         
2025-03-14T05:10:12.1594200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:12.1594740Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:12.1594975Z         
2025-03-14T05:10:12.1595269Z 
2025-03-14T05:10:12.1595363Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:12.1597573Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:10:12.1599821Z         l_features_p2_ = L_features_p2_
2025-03-14T05:10:12.1600046Z         l_features_p3_ = L_features_p3_
2025-03-14T05:10:12.1600262Z         l_features_p4_ = L_features_p4_
2025-03-14T05:10:12.1600470Z         l_features_p5_ = L_features_p5_
2025-03-14T05:10:12.1600678Z         l_features_p6_ = L_features_p6_
2025-03-14T05:10:12.1601058Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:10:12.1601608Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:10:12.1602147Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:10:12.1602682Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:10:12.1603216Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:10:12.1603726Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:10:12.1604224Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:10:12.1604776Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:10:12.1605357Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:10:12.1605915Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:10:12.1606452Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:10:12.1606811Z         
2025-03-14T05:10:12.1607357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1608001Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:10:12.1608272Z         
2025-03-14T05:10:12.1608655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1609143Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:12.1609400Z         
2025-03-14T05:10:12.1609919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1610588Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:10:12.1610859Z         
2025-03-14T05:10:12.1611235Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1611716Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:10:12.1611972Z         
2025-03-14T05:10:12.1612448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1613042Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:10:12.1613370Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:10:12.1613636Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:10:12.1613871Z         
2025-03-14T05:10:12.1614286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1614788Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:10:12.1615031Z         
2025-03-14T05:10:12.1615435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1615930Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:10:12.1616168Z         
2025-03-14T05:10:12.1616621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1617247Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:10:12.1617568Z         
2025-03-14T05:10:12.1618086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1618679Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:10:12.1619166Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:10:12.1620276Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:10:12.1620588Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:10:12.1621074Z         
2025-03-14T05:10:12.1621815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1622671Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:10:12.1622944Z         
2025-03-14T05:10:12.1623565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1624359Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:10:12.1624816Z         
2025-03-14T05:10:12.1625519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1626319Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:10:12.1626630Z         
2025-03-14T05:10:12.1627183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1627952Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:10:12.1628221Z         
2025-03-14T05:10:12.1628882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1629675Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:10:12.1630191Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:10:12.1630486Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:10:12.1630723Z         
2025-03-14T05:10:12.1631336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1631844Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:10:12.1632093Z         
2025-03-14T05:10:12.1632498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1632994Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:10:12.1633242Z         
2025-03-14T05:10:12.1633701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1634329Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:10:12.1634652Z         
2025-03-14T05:10:12.1635201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1635806Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:10:12.1636293Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:10:12.1636771Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:10:12.1637065Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:10:12.1637299Z         
2025-03-14T05:10:12.1637810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1638438Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:10:12.1638701Z         
2025-03-14T05:10:12.1639072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1639546Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:10:12.1639800Z         
2025-03-14T05:10:12.1640302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1640914Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:10:12.1641191Z         
2025-03-14T05:10:12.1641560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1642028Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:10:12.1642279Z         
2025-03-14T05:10:12.1642735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1643334Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:10:12.1643674Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:10:12.1643940Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:10:12.1644180Z         
2025-03-14T05:10:12.1644589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1645092Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:10:12.1645332Z         
2025-03-14T05:10:12.1645741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1646235Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:10:12.1646478Z         
2025-03-14T05:10:12.1646933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1647561Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:10:12.1647887Z         
2025-03-14T05:10:12.1648397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1649001Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:10:12.1649487Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:10:12.1649961Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:10:12.1650254Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:10:12.1650487Z         
2025-03-14T05:10:12.1650992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1651606Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:10:12.1651869Z         
2025-03-14T05:10:12.1652247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1652722Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:10:12.1652973Z         
2025-03-14T05:10:12.1653470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1654079Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:10:12.1654362Z         
2025-03-14T05:10:12.1654728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1655195Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:10:12.1655446Z         
2025-03-14T05:10:12.1655826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1656023Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:10:12.1656122Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:10:12.1656248Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:10:12.1656316Z         
2025-03-14T05:10:12.1656643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1656767Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:10:12.1656840Z         
2025-03-14T05:10:12.1657154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1657280Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:10:12.1657346Z         
2025-03-14T05:10:12.1657723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1657926Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:10:12.1657999Z         
2025-03-14T05:10:12.1658428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1658578Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:10:12.1658877Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:10:12.1659004Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:10:12.1659114Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:10:12.1659185Z         
2025-03-14T05:10:12.1659596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1659746Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:12.1659810Z         
2025-03-14T05:10:12.1660096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1660228Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:10:12.1660300Z         
2025-03-14T05:10:12.1660710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:10:12.1660857Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:10:12.1660943Z         
2025-03-14T05:10:12.1661223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1661361Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:10:12.1661424Z         
2025-03-14T05:10:12.1661805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:10:12.1661992Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:10:12.1662097Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:10:12.1662213Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:10:12.1662286Z         
2025-03-14T05:10:12.1662603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:10:12.1662732Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:10:12.1662796Z         
2025-03-14T05:10:12.1663117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:10:12.1663239Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:10:12.1663311Z         
2025-03-14T05:10:12.1663676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:10:12.1663892Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:10:12.1663959Z         
2025-03-14T05:10:12.1664499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:10:12.1664653Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:10:12.1664969Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:10:12.1665094Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:10:12.1665219Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:10:12.1665292Z         
2025-03-14T05:10:12.1665592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:12.1665716Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:10:12.1665852Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:10:12.1665972Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:10:12.1666103Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:10:12.1666219Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:10:12.1666298Z         
2025-03-14T05:10:12.1666567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1666991Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:10:12.1667088Z         
2025-03-14T05:10:12.1667371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1667565Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:10:12.1667646Z         
2025-03-14T05:10:12.1668027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1668419Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1668497Z         
2025-03-14T05:10:12.1668842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1669244Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:10:12.1669310Z         
2025-03-14T05:10:12.1669571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1669957Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:10:12.1670031Z         
2025-03-14T05:10:12.1670314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1670534Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:10:12.1670598Z         
2025-03-14T05:10:12.1670976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1671365Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1671436Z         
2025-03-14T05:10:12.1671795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1672184Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:10:12.1672258Z         
2025-03-14T05:10:12.1672513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1672901Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:10:12.1672964Z         
2025-03-14T05:10:12.1673242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1673432Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:10:12.1673508Z         
2025-03-14T05:10:12.1673869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1674279Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1674344Z         
2025-03-14T05:10:12.1674697Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1675073Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:10:12.1675145Z         
2025-03-14T05:10:12.1675388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1675769Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:10:12.1675841Z         
2025-03-14T05:10:12.1676105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1676284Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:10:12.1676348Z         
2025-03-14T05:10:12.1676727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1677127Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:10:12.1677200Z         
2025-03-14T05:10:12.1677545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1677923Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:10:12.1677989Z         
2025-03-14T05:10:12.1678241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:10:12.1678797Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:10:12.1678868Z         
2025-03-14T05:10:12.1679137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:10:12.1679305Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:10:12.1679396Z         
2025-03-14T05:10:12.1679758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:10:12.1680388Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:10:12.1680453Z         
2025-03-14T05:10:12.1680799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:10:12.1681367Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:10:12.1681605Z         
2025-03-14T05:10:12.1681947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:10:12.1682119Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:10:12.1682261Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:10:12.1682431Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:10:12.1682576Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:10:12.1682779Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:10:12.1682945Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:10:12.1683092Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:10:12.1683233Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:10:12.1683375Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:10:12.1683514Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:10:12.1683579Z         
2025-03-14T05:10:12.1684003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:10:12.1684178Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:10:12.1684371Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:10:12.1684548Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:10:12.1684718Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:10:12.1684891Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:10:12.1685067Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:10:12.1685290Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:10:12.1685460Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:10:12.1685624Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:10:12.1685798Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:10:12.1685958Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:10:12.1686130Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:10:12.1686270Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:10:12.1686434Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:10:12.1686593Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:10:12.1686667Z         
2025-03-14T05:10:12.1687065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1687278Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:10:12.1687347Z         
2025-03-14T05:10:12.1687773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1687933Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:10:12.1688094Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:12.1688261Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:12.1688323Z         
2025-03-14T05:10:12.1688692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1688857Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:12.1688928Z         
2025-03-14T05:10:12.1689231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1689382Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:12.1689446Z         
2025-03-14T05:10:12.1689756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1689888Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1690022Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1690170Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:12.1690243Z         
2025-03-14T05:10:12.1690549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1690677Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1690814Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1690975Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:10:12.1691040Z         
2025-03-14T05:10:12.1691360Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1691495Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1691594Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:12.1691719Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:10:12.1691791Z         
2025-03-14T05:10:12.1692090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1692246Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:12.1692336Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:12.1692472Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:10:12.1692537Z         
2025-03-14T05:10:12.1692866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1693022Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1693147Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:10:12.1693210Z         
2025-03-14T05:10:12.1693508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1693657Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1693793Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:10:12.1693872Z         
2025-03-14T05:10:12.1694169Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1694326Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1694435Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:10:12.1694506Z         
2025-03-14T05:10:12.1694803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1694988Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:12.1695099Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:10:12.1695171Z         
2025-03-14T05:10:12.1695501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1695649Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:12.1695712Z         
2025-03-14T05:10:12.1696040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1696172Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:12.1696243Z         
2025-03-14T05:10:12.1696576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1696739Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:12.1696864Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:10:12.1697021Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:12.1697175Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:10:12.1697248Z         
2025-03-14T05:10:12.1697591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1697738Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:12.1697861Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:10:12.1698017Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:12.1698153Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:10:12.1698227Z         
2025-03-14T05:10:12.1698553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1698678Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:12.1698833Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:12.1698972Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:10:12.1699035Z         
2025-03-14T05:10:12.1699364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1699492Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:12.1699679Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:12.1699812Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:10:12.1699886Z         
2025-03-14T05:10:12.1700188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1700293Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:12.1700409Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:12.1700483Z         
2025-03-14T05:10:12.1700781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1700885Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:12.1701007Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:12.1701073Z         
2025-03-14T05:10:12.1701374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1701485Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:12.1701621Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:12.1701684Z         
2025-03-14T05:10:12.1701981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1702092Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:12.1702245Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:12.1702311Z         
2025-03-14T05:10:12.1702652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1702844Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:12.1702915Z         
2025-03-14T05:10:12.1703236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1703402Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:12.1703465Z         
2025-03-14T05:10:12.1703847Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1704036Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:12.1705024Z         
2025-03-14T05:10:12.1705603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1705831Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:12.1705901Z         
2025-03-14T05:10:12.1706333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1706492Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:10:12.1706708Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:12.1706874Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:12.1706951Z         
2025-03-14T05:10:12.1707310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1707486Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:12.1707551Z         
2025-03-14T05:10:12.1707858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1708004Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:12.1708077Z         
2025-03-14T05:10:12.1708381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1708523Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1708658Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1708816Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:10:12.1708881Z         
2025-03-14T05:10:12.1709197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1709327Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1709472Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1709631Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:10:12.1709698Z         
2025-03-14T05:10:12.1710002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1710140Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1710241Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:12.1710370Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:10:12.1710440Z         
2025-03-14T05:10:12.1710741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1710897Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:12.1710992Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:12.1711125Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:10:12.1711191Z         
2025-03-14T05:10:12.1711496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1711651Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1711773Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:10:12.1711837Z         
2025-03-14T05:10:12.1712133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1712283Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1712431Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:10:12.1712513Z         
2025-03-14T05:10:12.1712816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1712967Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1713086Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:10:12.1713149Z         
2025-03-14T05:10:12.1713451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1713631Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:12.1713751Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:10:12.1713815Z         
2025-03-14T05:10:12.1714155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1714295Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:12.1714368Z         
2025-03-14T05:10:12.1714694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1714840Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:12.1714904Z         
2025-03-14T05:10:12.1715249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1715410Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:12.1715536Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:10:12.1715699Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:12.1715853Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:10:12.1715924Z         
2025-03-14T05:10:12.1716260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1716405Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:12.1716526Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:10:12.1716684Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:12.1716823Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:10:12.1716896Z         
2025-03-14T05:10:12.1717217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1717340Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:12.1717497Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:12.1717639Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:10:12.1717702Z         
2025-03-14T05:10:12.1718031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1718158Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:12.1718342Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:12.1718474Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:10:12.1718546Z         
2025-03-14T05:10:12.1718845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1718952Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:12.1719069Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:12.1719140Z         
2025-03-14T05:10:12.1719437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1719542Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:12.1719660Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:12.1719731Z         
2025-03-14T05:10:12.1720028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1720151Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:12.1720281Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:12.1720353Z         
2025-03-14T05:10:12.1720645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1720781Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:12.1720912Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:12.1720985Z         
2025-03-14T05:10:12.1721317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1721527Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:12.1721592Z         
2025-03-14T05:10:12.1721921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1722083Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:12.1722158Z         
2025-03-14T05:10:12.1722527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1722706Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:12.1722769Z         
2025-03-14T05:10:12.1723164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1723373Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:12.1723439Z         
2025-03-14T05:10:12.1723861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1724024Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:10:12.1724197Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:12.1724333Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:12.1724403Z         
2025-03-14T05:10:12.1724761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1724931Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:10:12.1724994Z         
2025-03-14T05:10:12.1725301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1725442Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:12.1725512Z         
2025-03-14T05:10:12.1725811Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1725945Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1726067Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1726219Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:10:12.1726284Z         
2025-03-14T05:10:12.1726595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1727364Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1727493Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1727640Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:10:12.1727717Z         
2025-03-14T05:10:12.1728035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1728163Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1728255Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:12.1728390Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:10:12.1728454Z         
2025-03-14T05:10:12.1728767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1728915Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:12.1729020Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:12.1729147Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:10:12.1729220Z         
2025-03-14T05:10:12.1729519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1729682Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1729794Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:10:12.1729867Z         
2025-03-14T05:10:12.1730158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1730330Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1730466Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:10:12.1730530Z         
2025-03-14T05:10:12.1730832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1730975Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1731089Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:10:12.1731154Z         
2025-03-14T05:10:12.1731458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1731640Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:12.1731757Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:10:12.1731823Z         
2025-03-14T05:10:12.1732157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1732293Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:12.1732363Z         
2025-03-14T05:10:12.1732706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1732848Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:12.1732910Z         
2025-03-14T05:10:12.1733271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1733404Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:12.1733535Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:10:12.1733708Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:12.1733857Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:10:12.1733921Z         
2025-03-14T05:10:12.1734266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1734399Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:12.1734530Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:10:12.1734678Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:12.1734821Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:10:12.1734885Z         
2025-03-14T05:10:12.1735217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1735330Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:12.1735495Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:12.1735628Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:10:12.1735703Z         
2025-03-14T05:10:12.1736040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1736173Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:12.1736341Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:12.1736472Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:10:12.1736543Z         
2025-03-14T05:10:12.1736842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1736945Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:12.1737057Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:12.1737130Z         
2025-03-14T05:10:12.1737425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1737528Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:12.1737641Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:12.1737712Z         
2025-03-14T05:10:12.1738009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1738129Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:12.1738258Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:12.1738329Z         
2025-03-14T05:10:12.1738618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1738752Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:12.1738882Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:12.1738953Z         
2025-03-14T05:10:12.1739303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1739495Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:12.1739560Z         
2025-03-14T05:10:12.1739889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1740047Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:12.1740117Z         
2025-03-14T05:10:12.1740483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1740662Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:12.1740727Z         
2025-03-14T05:10:12.1741114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1741315Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:10:12.1741390Z         
2025-03-14T05:10:12.1741822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1741994Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:10:12.1742139Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:12.1742282Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:12.1742345Z         
2025-03-14T05:10:12.1742710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1742874Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:10:12.1742944Z         
2025-03-14T05:10:12.1743248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1743403Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:12.1743467Z         
2025-03-14T05:10:12.1743779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1743914Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1744042Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1744299Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:10:12.1744373Z         
2025-03-14T05:10:12.1744706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1744857Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1744990Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1745140Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:10:12.1745215Z         
2025-03-14T05:10:12.1745573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1745701Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1745790Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:12.1745924Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:10:12.1745988Z         
2025-03-14T05:10:12.1746311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1746458Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:12.1746561Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:12.1746689Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:10:12.1746763Z         
2025-03-14T05:10:12.1747072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1747231Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1747344Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:10:12.1747415Z         
2025-03-14T05:10:12.1747719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1747890Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1748022Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:10:12.1748096Z         
2025-03-14T05:10:12.1748403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1748560Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1748669Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:10:12.1748742Z         
2025-03-14T05:10:12.1749047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1749244Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:12.1749356Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:10:12.1749427Z         
2025-03-14T05:10:12.1749764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1749910Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:12.1749977Z         
2025-03-14T05:10:12.1750318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1750460Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:12.1750543Z         
2025-03-14T05:10:12.1750897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1751036Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:12.1751169Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:10:12.1751347Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:12.1751496Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:10:12.1751559Z         
2025-03-14T05:10:12.1751915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1752054Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:12.1752182Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:10:12.1752332Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:12.1752475Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:10:12.1752540Z         
2025-03-14T05:10:12.1752874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1752988Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:12.1753153Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:12.1753286Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:10:12.1753358Z         
2025-03-14T05:10:12.1753699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1753836Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:12.1754001Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:12.1754141Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:10:12.1754205Z         
2025-03-14T05:10:12.1754523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1754620Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:12.1754744Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:12.1754809Z         
2025-03-14T05:10:12.1755125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1755222Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:12.1755344Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:12.1755410Z         
2025-03-14T05:10:12.1755725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1755841Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:12.1755980Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:12.1756044Z         
2025-03-14T05:10:12.1756381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1756498Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:12.1756636Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:12.1756703Z         
2025-03-14T05:10:12.1757071Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1757262Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:12.1757335Z         
2025-03-14T05:10:12.1757662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1757835Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:12.1757901Z         
2025-03-14T05:10:12.1758291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1758465Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:12.1758529Z         
2025-03-14T05:10:12.1758921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:12.1759123Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:10:12.1759196Z         
2025-03-14T05:10:12.1759629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:12.1759797Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:10:12.1759941Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:12.1760082Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:12.1760146Z         
2025-03-14T05:10:12.1760512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:12.1760675Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:10:12.1760747Z         
2025-03-14T05:10:12.1761050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:12.1761196Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:12.1761259Z         
2025-03-14T05:10:12.1761571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:12.1761698Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:12.1761826Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1761968Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:10:12.1762040Z         
2025-03-14T05:10:12.1762348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:12.1762492Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:12.1762608Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:12.1762759Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:10:12.1762823Z         
2025-03-14T05:10:12.1763147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:12.1763266Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:12.1763362Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:12.1763490Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:10:12.1763562Z         
2025-03-14T05:10:12.1763863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:12.1764015Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:12.1764103Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:12.1764234Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:10:12.1764299Z         
2025-03-14T05:10:12.1764598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:12.1764747Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:12.1764869Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:10:12.1764934Z         
2025-03-14T05:10:12.1765249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:12.1765421Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:12.1765531Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:10:12.1765602Z         
2025-03-14T05:10:12.1765897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:12.1766049Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:12.1766154Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:10:12.1766226Z         
2025-03-14T05:10:12.1766518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:12.1766707Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:12.1766814Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:10:12.1766884Z         
2025-03-14T05:10:12.1767205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:12.1767344Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:12.1767407Z         
2025-03-14T05:10:12.1767731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:12.1767859Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:12.1767946Z         
2025-03-14T05:10:12.1768278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:12.1768419Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:12.1768557Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:10:12.1768713Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:12.1768848Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:10:12.1768920Z         
2025-03-14T05:10:12.1769253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:12.1769397Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:12.1769517Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:10:12.1769668Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:12.1769801Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:10:12.1769874Z         
2025-03-14T05:10:12.1770192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:12.1770313Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:12.1771178Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:12.1771331Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:10:12.1771397Z         
2025-03-14T05:10:12.1771758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:12.1771896Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:12.1772061Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:12.1772196Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:10:12.1772260Z         
2025-03-14T05:10:12.1772580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:12.1772676Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:12.1772797Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:12.1772861Z         
2025-03-14T05:10:12.1773171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:12.1773266Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:12.1773387Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:12.1773453Z         
2025-03-14T05:10:12.1773759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:12.1773871Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:12.1774005Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:12.1774087Z         
2025-03-14T05:10:12.1774392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:12.1774504Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:12.1774637Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:12.1774700Z         
2025-03-14T05:10:12.1775057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:12.1775238Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:12.1775311Z         
2025-03-14T05:10:12.1775631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:12.1775795Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:12.1775859Z         
2025-03-14T05:10:12.1776236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:12.1776403Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:12.1776473Z         
2025-03-14T05:10:12.1776943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:12.1777082Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:12.1777149Z         
2025-03-14T05:10:12.1777461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1777660Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:10:12.1777732Z         
2025-03-14T05:10:12.1778154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1778275Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:10:12.1778378Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:12.1778495Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:12.1778559Z         
2025-03-14T05:10:12.1779014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1779146Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1779377Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:10:12.1779453Z         
2025-03-14T05:10:12.1779891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1780062Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1780126Z         
2025-03-14T05:10:12.1780417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1780557Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:12.1780631Z         
2025-03-14T05:10:12.1781046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1781187Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:10:12.1781294Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:12.1782139Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:12.1782215Z         
2025-03-14T05:10:12.1782672Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1782807Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1783046Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:10:12.1783111Z         
2025-03-14T05:10:12.1783906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1784072Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1784188Z         
2025-03-14T05:10:12.1784485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1784621Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:12.1784746Z         
2025-03-14T05:10:12.1785196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1785306Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:10:12.1785423Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:12.1785538Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:12.1785614Z         
2025-03-14T05:10:12.1786060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1786201Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1786433Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:10:12.1786507Z         
2025-03-14T05:10:12.1786962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1787124Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1787195Z         
2025-03-14T05:10:12.1787483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1787613Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:12.1787711Z         
2025-03-14T05:10:12.1788134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1788246Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:10:12.1788358Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:12.1788496Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:12.1788569Z         
2025-03-14T05:10:12.1789006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1789146Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:12.1789377Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:10:12.1789451Z         
2025-03-14T05:10:12.1789893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1790058Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1790124Z         
2025-03-14T05:10:12.1790419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1790537Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:12.1790613Z         
2025-03-14T05:10:12.1791047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:12.1791178Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:10:12.1791281Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:12.1791401Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:12.1791465Z         
2025-03-14T05:10:12.1791912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:12.1792070Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:12.1792299Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:10:12.1792367Z         
2025-03-14T05:10:12.1792815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:12.1792982Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:12.1793046Z         
2025-03-14T05:10:12.1793338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:12.1793458Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:12.1793529Z         
2025-03-14T05:10:12.1793819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1794205Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:12.1794269Z         
2025-03-14T05:10:12.1794561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1795002Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:12.1795073Z         
2025-03-14T05:10:12.1795339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:12.1795545Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:10:12.1795611Z         
2025-03-14T05:10:12.1795993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:12.1796132Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:12.1796206Z         
2025-03-14T05:10:12.1796493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:12.1796646Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:10:12.1796713Z         
2025-03-14T05:10:12.1797099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:12.1797243Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:12.1797317Z         
2025-03-14T05:10:12.1797782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:12.1797923Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:10:12.1798051Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:12.1798202Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:12.1798339Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:12.1798403Z         
2025-03-14T05:10:12.1798766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:12.1798882Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:12.1798953Z         
2025-03-14T05:10:13.4127429Z 
2025-03-14T05:10:13.4132677Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:13.4136289Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T05:10:13.4138760Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:10:13.4144045Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:10:13.4148547Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T05:10:13.4152627Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T05:10:13.4156789Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T05:10:13.4157142Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T05:10:13.4157411Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T05:10:13.4157707Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T05:10:13.4157964Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T05:10:13.4158226Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T05:10:13.4158498Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:10:13.4158790Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T05:10:13.4159071Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T05:10:13.4159353Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T05:10:13.4159631Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T05:10:13.4159870Z         
2025-03-14T05:10:13.4160422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:13.4161191Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:10:13.4161534Z         
2025-03-14T05:10:13.4162236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:13.4162998Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:10:13.4163410Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:10:13.4163767Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:10:13.4164041Z         
2025-03-14T05:10:13.4164524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:13.4165235Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:10:13.4165587Z         
2025-03-14T05:10:13.4166040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:13.4166566Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:10:13.4166837Z         
2025-03-14T05:10:13.4167288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:13.4167792Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:13.4168105Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4168441Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:10:13.4168776Z         
2025-03-14T05:10:13.4169189Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:13.4169693Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:13.4170008Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:13.4170372Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:10:13.4170650Z         
2025-03-14T05:10:13.4171161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:13.4171641Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4171912Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:10:13.4172187Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:10:13.4172433Z         
2025-03-14T05:10:13.4172837Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:13.4173360Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:13.4173660Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:10:13.4173935Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:10:13.4174187Z         
2025-03-14T05:10:13.4174607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:13.4175134Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:13.4175458Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:10:13.4175690Z         
2025-03-14T05:10:13.4176094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:13.4176611Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:13.4176931Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:10:13.4177159Z         
2025-03-14T05:10:13.4177537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:13.4178035Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:13.4178356Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:10:13.4178588Z         
2025-03-14T05:10:13.4178973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:13.4179497Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:13.4179838Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:10:13.4180069Z         
2025-03-14T05:10:13.4180487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:13.4181008Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:13.4181264Z         
2025-03-14T05:10:13.4181891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:13.4182451Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:13.4182708Z         
2025-03-14T05:10:13.4183147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:13.4183725Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:13.4184060Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:10:13.4184499Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:13.4184867Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:10:13.4185149Z         
2025-03-14T05:10:13.4185587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:13.4186134Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:13.4186465Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:10:13.4186803Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:13.4187146Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:10:13.4187409Z         
2025-03-14T05:10:13.4187826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:13.4188332Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:13.4188668Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:13.4189044Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:10:13.4189328Z         
2025-03-14T05:10:13.4189748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:13.4190253Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:13.4190596Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:13.4190958Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:10:13.4191220Z         
2025-03-14T05:10:13.4191626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:13.4192094Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:13.4192367Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:13.4192606Z         
2025-03-14T05:10:13.4192999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:13.4193462Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:13.4193726Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:13.4193962Z         
2025-03-14T05:10:13.4194354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:13.4194832Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:13.4195136Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:13.4195410Z         
2025-03-14T05:10:13.4195809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:13.4196287Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:13.4196590Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:13.4196858Z         
2025-03-14T05:10:13.4197300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:13.4197889Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:13.4198190Z         
2025-03-14T05:10:13.4198651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:13.4199209Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:10:13.4199497Z         
2025-03-14T05:10:13.4199972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:13.4200584Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:10:13.4200887Z         
2025-03-14T05:10:13.4201374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:13.4202042Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T05:10:13.4202375Z         
2025-03-14T05:10:13.4202911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:13.4203607Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T05:10:13.4204011Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:10:13.4204367Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:10:13.4204632Z         
2025-03-14T05:10:13.4205096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:13.4205695Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:10:13.4205991Z         
2025-03-14T05:10:13.4206389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:13.4206906Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:10:13.4207177Z         
2025-03-14T05:10:13.4207585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:13.4208091Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:10:13.4208407Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4208742Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T05:10:13.4209035Z         
2025-03-14T05:10:13.4209433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:13.4209930Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:10:13.4210239Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:10:13.4210598Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T05:10:13.4210866Z         
2025-03-14T05:10:13.4211257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:13.4211737Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4212011Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:10:13.4212293Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T05:10:13.4212555Z         
2025-03-14T05:10:13.4212961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:13.4213480Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:10:13.4213787Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:10:13.4214073Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T05:10:13.4214331Z         
2025-03-14T05:10:13.4214726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:13.4215238Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:13.4215574Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T05:10:13.4215837Z         
2025-03-14T05:10:13.4216261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:13.4216768Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:13.4217097Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T05:10:13.4217339Z         
2025-03-14T05:10:13.4217723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:13.4218233Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:13.4218557Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T05:10:13.4218797Z         
2025-03-14T05:10:13.4219187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:13.4219733Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:10:13.4220087Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T05:10:13.4220323Z         
2025-03-14T05:10:13.4220747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:13.4221282Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:10:13.4221546Z         
2025-03-14T05:10:13.4221965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:13.4222517Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:10:13.4222787Z         
2025-03-14T05:10:13.4223219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:13.4223774Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:10:13.4224102Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T05:10:13.4224526Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:10:13.4224892Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T05:10:13.4225162Z         
2025-03-14T05:10:13.4225627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:13.4226173Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:10:13.4226503Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T05:10:13.4226848Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:10:13.4227205Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T05:10:13.4227468Z         
2025-03-14T05:10:13.4227891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:13.4228402Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:10:13.4228769Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:10:13.4229145Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T05:10:13.4229409Z         
2025-03-14T05:10:13.4229838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:13.4230341Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:10:13.4230681Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:10:13.4231041Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T05:10:13.4231303Z         
2025-03-14T05:10:13.4231712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:13.4232186Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:10:13.4232460Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:10:13.4232701Z         
2025-03-14T05:10:13.4233100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:13.4233565Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:10:13.4233830Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:10:13.4234070Z         
2025-03-14T05:10:13.4234464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:13.4234953Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:10:13.4235292Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:10:13.4235553Z         
2025-03-14T05:10:13.4235941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:13.4236416Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:10:13.4236737Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:10:13.4236998Z         
2025-03-14T05:10:13.4237433Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:13.4238029Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:10:13.4238338Z         
2025-03-14T05:10:13.4238766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:13.4239320Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:10:13.4239618Z         
2025-03-14T05:10:13.4240076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:13.4240666Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:10:13.4240955Z         
2025-03-14T05:10:13.4241425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:13.4242117Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T05:10:13.4242457Z         
2025-03-14T05:10:13.4242957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:13.4243615Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T05:10:13.4244001Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:10:13.4244340Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:10:13.4244597Z         
2025-03-14T05:10:13.4245052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:13.4245652Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T05:10:13.4245939Z         
2025-03-14T05:10:13.4246322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:13.4246825Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:10:13.4247094Z         
2025-03-14T05:10:13.4247489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:13.4247979Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:10:13.4248284Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4248644Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T05:10:13.4248913Z         
2025-03-14T05:10:13.4249311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:13.4249811Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:10:13.4250116Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:10:13.4250443Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T05:10:13.4250708Z         
2025-03-14T05:10:13.4251104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:13.4251587Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4251852Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:10:13.4252127Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T05:10:13.4252379Z         
2025-03-14T05:10:13.4252774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:13.4253290Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:10:13.4253594Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:10:13.4253881Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T05:10:13.4254125Z         
2025-03-14T05:10:13.4254510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:13.4255032Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:13.4255369Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T05:10:13.4255605Z         
2025-03-14T05:10:13.4255981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:13.4256474Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:13.4256788Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T05:10:13.4257022Z         
2025-03-14T05:10:13.4257398Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:13.4257895Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:13.4258217Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T05:10:13.4258449Z         
2025-03-14T05:10:13.4258827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:13.4259352Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:10:13.4259692Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T05:10:13.4259926Z         
2025-03-14T05:10:13.4260337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:13.4260860Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:10:13.4261149Z         
2025-03-14T05:10:13.4261559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:13.4262074Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:10:13.4262328Z         
2025-03-14T05:10:13.4262766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:13.4263296Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:10:13.4263613Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T05:10:13.4263957Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:10:13.4264396Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T05:10:13.4264664Z         
2025-03-14T05:10:13.4265136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:13.4265680Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:10:13.4266039Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T05:10:13.4266386Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:10:13.4266739Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T05:10:13.4267008Z         
2025-03-14T05:10:13.4267430Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:13.4267967Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:10:13.4268328Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:10:13.4268688Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T05:10:13.4268960Z         
2025-03-14T05:10:13.4269395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:13.4269902Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:10:13.4270245Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:10:13.4270606Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T05:10:13.4270866Z         
2025-03-14T05:10:13.4271295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:13.4271769Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:10:13.4272039Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:10:13.4272285Z         
2025-03-14T05:10:13.4272683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:13.4273149Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:10:13.4273414Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:10:13.4273659Z         
2025-03-14T05:10:13.4274055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:13.4274567Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:10:13.4274877Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:10:13.4275134Z         
2025-03-14T05:10:13.4275527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:13.4276030Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:10:13.4276338Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:10:13.4276593Z         
2025-03-14T05:10:13.4277030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:13.4277631Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:10:13.4277938Z         
2025-03-14T05:10:13.4278384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:13.4278935Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:10:13.4279221Z         
2025-03-14T05:10:13.4279690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:13.4280294Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:10:13.4280591Z         
2025-03-14T05:10:13.4281098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:13.4282005Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T05:10:13.4282340Z         
2025-03-14T05:10:13.4282865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:13.4283543Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T05:10:13.4283940Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:10:13.4284286Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:10:13.4284550Z         
2025-03-14T05:10:13.4285003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:13.4285589Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:10:13.4285880Z         
2025-03-14T05:10:13.4286262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:13.4286762Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:10:13.4287027Z         
2025-03-14T05:10:13.4287418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:13.4287909Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:10:13.4288271Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4288602Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T05:10:13.4288867Z         
2025-03-14T05:10:13.4289261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:13.4289780Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:10:13.4290088Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:10:13.4290417Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T05:10:13.4290687Z         
2025-03-14T05:10:13.4291078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:13.4291557Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4291825Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:10:13.4292097Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T05:10:13.4292348Z         
2025-03-14T05:10:13.4292739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:13.4293244Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:10:13.4293539Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:10:13.4293813Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T05:10:13.4294064Z         
2025-03-14T05:10:13.4294451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:13.4294981Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:13.4295330Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T05:10:13.4295569Z         
2025-03-14T05:10:13.4295952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:13.4296445Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:13.4296764Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T05:10:13.4296999Z         
2025-03-14T05:10:13.4297383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:13.4297882Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:13.4298196Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T05:10:13.4298426Z         
2025-03-14T05:10:13.4298806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:13.4299338Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:10:13.4299677Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T05:10:13.4299911Z         
2025-03-14T05:10:13.4300322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:13.4300865Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:10:13.4301127Z         
2025-03-14T05:10:13.4301544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:13.4302062Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:10:13.4302324Z         
2025-03-14T05:10:13.4302767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:13.4303291Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:10:13.4303606Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T05:10:13.4303939Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:10:13.4304370Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T05:10:13.4304637Z         
2025-03-14T05:10:13.4305073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:13.4305614Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:10:13.4305937Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T05:10:13.4306273Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:10:13.4306631Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T05:10:13.4306896Z         
2025-03-14T05:10:13.4307349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:13.4307868Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:10:13.4308200Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:10:13.4308555Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T05:10:13.4308835Z         
2025-03-14T05:10:13.4309248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:13.4309745Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:10:13.4310081Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:10:13.4310439Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T05:10:13.4310697Z         
2025-03-14T05:10:13.4311097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:13.4311561Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:10:13.4311835Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:10:13.4312081Z         
2025-03-14T05:10:13.4312474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:13.4312925Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:10:13.4313193Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:10:13.4313432Z         
2025-03-14T05:10:13.4313852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:13.4314474Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:10:13.4315103Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:10:13.4315593Z         
2025-03-14T05:10:13.4316094Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:13.4316662Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:10:13.4317044Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:10:13.4317399Z         
2025-03-14T05:10:13.4317912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:13.4318563Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:10:13.4318958Z         
2025-03-14T05:10:13.4319432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:13.4320035Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:10:13.4320396Z         
2025-03-14T05:10:13.4321041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:13.4321718Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:10:13.4322078Z         
2025-03-14T05:10:13.4322637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:10:13.4323395Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T05:10:13.4323782Z         
2025-03-14T05:10:13.4324362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:10:13.4325094Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T05:10:13.4325549Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:10:13.4325982Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:10:13.4326403Z         
2025-03-14T05:10:13.4326917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:13.4327596Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T05:10:13.4327939Z         
2025-03-14T05:10:13.4328439Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:13.4329011Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:10:13.4329345Z         
2025-03-14T05:10:13.4329819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:13.4330402Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:10:13.4330759Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4331275Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T05:10:13.4331610Z         
2025-03-14T05:10:13.4332102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:13.4332660Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:10:13.4333025Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:10:13.4333442Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T05:10:13.4333780Z         
2025-03-14T05:10:13.4334230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:13.4334818Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:10:13.4335147Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:10:13.4335525Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T05:10:13.4335952Z         
2025-03-14T05:10:13.4336412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:13.4337018Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:10:13.4337376Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:10:13.4337686Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T05:10:13.4338027Z         
2025-03-14T05:10:13.4338507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:13.4339148Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:13.4339529Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T05:10:13.4339825Z         
2025-03-14T05:10:13.4340371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:13.4340941Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:13.4341326Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T05:10:13.4341658Z         
2025-03-14T05:10:13.4342110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:13.4342671Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:13.4343124Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T05:10:13.4343425Z         
2025-03-14T05:10:13.4343882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:13.4344574Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:10:13.4345000Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T05:10:13.4369209Z         
2025-03-14T05:10:13.4369870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:13.4370556Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:10:13.4370839Z         
2025-03-14T05:10:13.4371290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:13.4371878Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:10:13.4372130Z         
2025-03-14T05:10:13.4372565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:13.4373099Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:10:13.4373419Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T05:10:13.4373752Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:10:13.4374102Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T05:10:13.4374366Z         
2025-03-14T05:10:13.4374812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:13.4375344Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:10:13.4375659Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T05:10:13.4375987Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:10:13.4376327Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T05:10:13.4376591Z         
2025-03-14T05:10:13.4377051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:13.4377583Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:10:13.4377914Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:10:13.4378264Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T05:10:13.4378521Z         
2025-03-14T05:10:13.4378946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:13.4379444Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:10:13.4379781Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:10:13.4380148Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T05:10:13.4380395Z         
2025-03-14T05:10:13.4380792Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:13.4381250Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:10:13.4381717Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:10:13.4381966Z         
2025-03-14T05:10:13.4382367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:13.4382827Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:10:13.4383098Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:10:13.4383394Z         
2025-03-14T05:10:13.4383783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:13.4384311Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:10:13.4384620Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:10:13.4384877Z         
2025-03-14T05:10:13.4385311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:13.4385791Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:10:13.4386098Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:10:13.4386356Z         
2025-03-14T05:10:13.4386802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:13.4387387Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:10:13.4387691Z         
2025-03-14T05:10:13.4388107Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:13.4388685Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:10:13.4388972Z         
2025-03-14T05:10:13.4389437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:10:13.4390056Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:10:13.4390339Z         
2025-03-14T05:10:13.4390926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:10:13.4391631Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:10:13.4391874Z         
2025-03-14T05:10:13.4392263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4392747Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:10:13.4393001Z         
2025-03-14T05:10:13.4393518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:13.4394170Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:10:13.4394506Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:10:13.4394774Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:10:13.4395009Z         
2025-03-14T05:10:13.4395545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:13.4396174Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:13.4396583Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T05:10:13.4396925Z         
2025-03-14T05:10:13.4397480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:13.4398145Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:13.4398427Z         
2025-03-14T05:10:13.4398829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4399293Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:10:13.4399532Z         
2025-03-14T05:10:13.4400042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:13.4400677Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T05:10:13.4401009Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:10:13.4401294Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:10:13.4401533Z         
2025-03-14T05:10:13.4402070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:13.4402693Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:13.4403112Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T05:10:13.4403463Z         
2025-03-14T05:10:13.4404030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:13.4404734Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:13.4405014Z         
2025-03-14T05:10:13.4405393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4405861Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:10:13.4406111Z         
2025-03-14T05:10:13.4406633Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:13.4407300Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T05:10:13.4407636Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:10:13.4407916Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:10:13.4408160Z         
2025-03-14T05:10:13.4408706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:13.4409338Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:13.4409763Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T05:10:13.4410113Z         
2025-03-14T05:10:13.4410648Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:13.4411337Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:13.4411623Z         
2025-03-14T05:10:13.4412004Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4412496Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:10:13.4412742Z         
2025-03-14T05:10:13.4413262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:13.4413914Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T05:10:13.4414248Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:10:13.4414532Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:10:13.4414775Z         
2025-03-14T05:10:13.4415317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:13.4415950Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:10:13.4416372Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T05:10:13.4416723Z         
2025-03-14T05:10:13.4417259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:13.4417958Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:13.4418267Z         
2025-03-14T05:10:13.4418650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4419128Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:10:13.4419373Z         
2025-03-14T05:10:13.4419890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:10:13.4420533Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T05:10:13.4420859Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:10:13.4421130Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:10:13.4421358Z         
2025-03-14T05:10:13.4421885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:10:13.4422540Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:10:13.4422972Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T05:10:13.4423312Z         
2025-03-14T05:10:13.4423826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:10:13.4424594Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:10:13.4424880Z         
2025-03-14T05:10:13.4425269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:10:13.4425753Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:10:13.4426036Z         
2025-03-14T05:10:13.4426400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:13.4427094Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:10:13.4427569Z         
2025-03-14T05:10:13.4427934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:13.4428700Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:10:13.4429264Z         
2025-03-14T05:10:13.4429608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:13.4430118Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T05:10:13.4430416Z         
2025-03-14T05:10:13.4430870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:10:13.4431457Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:10:13.4431739Z         
2025-03-14T05:10:13.4432117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:10:13.4432599Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T05:10:13.4432856Z         
2025-03-14T05:10:13.4433313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:10:13.4433864Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:10:13.4434110Z         
2025-03-14T05:10:13.4434678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:10:13.4435352Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:10:13.4435657Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:13.4435977Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:10:13.4436310Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:10:13.4436558Z         
2025-03-14T05:10:13.4437002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:10:13.4437523Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:10:13.4437780Z         
2025-03-14T05:10:19.7162472Z 
2025-03-14T05:10:19.7167378Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:19.7173815Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:19.7175978Z         l_stack0_ = L_stack0_
2025-03-14T05:10:19.7178546Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:10:19.7180314Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:10:19.7183650Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:10:19.7185679Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:10:19.7186324Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:10:19.7187161Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:10:19.7187876Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:10:19.7188494Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:10:19.7189000Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7189435Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7189874Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7190290Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7190612Z         
2025-03-14T05:10:19.7191042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:10:19.7191565Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:10:19.7192314Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:10:19.7193086Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:10:19.7193855Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:10:19.7194646Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:10:19.7194955Z         
2025-03-14T05:10:19.7195362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:10:19.7196384Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:10:19.7197088Z         
2025-03-14T05:10:19.7197506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:10:19.7198502Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:10:19.7199231Z         
2025-03-14T05:10:19.7199611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7200074Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7200341Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:19.7200584Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:19.7200867Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7201128Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:19.7201370Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:19.7201668Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7201939Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:19.7202176Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:19.7202450Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7202704Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:19.7202940Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:19.7203163Z         
2025-03-14T05:10:19.7203542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:19.7204322Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:19.7204879Z         
2025-03-14T05:10:19.7205347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:19.7205944Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:10:19.7206233Z         
2025-03-14T05:10:19.7206644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:19.7207200Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:19.7207496Z         
2025-03-14T05:10:19.7207918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:19.7208469Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:19.7208802Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7209147Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:19.7209428Z         
2025-03-14T05:10:19.7209857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:19.7210395Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:19.7210697Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:19.7211027Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:19.7211302Z         
2025-03-14T05:10:19.7211699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:19.7212197Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7212479Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:19.7212760Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:19.7213010Z         
2025-03-14T05:10:19.7213410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:19.7213924Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:19.7214231Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:19.7214527Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:19.7214791Z         
2025-03-14T05:10:19.7215222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:19.7215727Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:19.7216061Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:19.7216304Z         
2025-03-14T05:10:19.7216695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:19.7217204Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:19.7217528Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:19.7217764Z         
2025-03-14T05:10:19.7218158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:19.7218661Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:19.7218985Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:19.7219219Z         
2025-03-14T05:10:19.7219613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:19.7220147Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:19.7220498Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:19.7220751Z         
2025-03-14T05:10:19.7221173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:19.7221706Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:19.7221963Z         
2025-03-14T05:10:19.7222393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:19.7222915Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:19.7223169Z         
2025-03-14T05:10:19.7223598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:19.7224286Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:19.7224652Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:19.7225012Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:19.7225391Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:19.7225655Z         
2025-03-14T05:10:19.7226093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:19.7226640Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:19.7226960Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:19.7227291Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:19.7227668Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:19.7227962Z         
2025-03-14T05:10:19.7228388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:19.7228918Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:19.7229263Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:19.7229611Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:19.7229875Z         
2025-03-14T05:10:19.7230299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:19.7230822Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:19.7231168Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:19.7231527Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:19.7231790Z         
2025-03-14T05:10:19.7232196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:19.7232664Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:19.7232938Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:19.7233179Z         
2025-03-14T05:10:19.7233582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:19.7234069Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:19.7234336Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:19.7234580Z         
2025-03-14T05:10:19.7234984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:19.7235467Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:19.7235781Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:19.7236040Z         
2025-03-14T05:10:19.7236434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:19.7236915Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:19.7237207Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:19.7237459Z         
2025-03-14T05:10:19.7237904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:19.7238495Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:19.7238792Z         
2025-03-14T05:10:19.7239213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:19.7239773Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:19.7240066Z         
2025-03-14T05:10:19.7240520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:19.7241221Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:19.7241658Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:19.7241946Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:19.7242245Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:19.7242553Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:19.7242811Z         
2025-03-14T05:10:19.7243195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7243757Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7244111Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:19.7244358Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:19.7244730Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7245097Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:19.7245351Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:19.7245730Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7246090Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:19.7246339Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:19.7246721Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7247084Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:19.7247349Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:19.7247595Z         
2025-03-14T05:10:19.7248017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:19.7248619Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:10:19.7248930Z         
2025-03-14T05:10:19.7249415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:19.7250813Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:19.7251250Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:19.7251544Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:19.7251854Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:19.7252167Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:19.7252431Z         
2025-03-14T05:10:19.7253879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:19.7254632Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:19.7254980Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:19.7255319Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:19.7255654Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:19.7255952Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:19.7256198Z         
2025-03-14T05:10:19.7256687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:19.7257251Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:19.7257498Z         
2025-03-14T05:10:19.7257648Z 
2025-03-14T05:10:19.7257752Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:19.7259679Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:19.7261706Z         l_stack0_ = L_stack0_
2025-03-14T05:10:19.7262062Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:10:19.7262544Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:10:19.7263041Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:10:19.7263516Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:10:19.7264038Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:10:19.7264704Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:10:19.7265330Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:10:19.7265938Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:10:19.7266453Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7266870Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7267283Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7267686Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7267989Z         
2025-03-14T05:10:19.7268372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:10:19.7268861Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:10:19.7269574Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:10:19.7270320Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:10:19.7271070Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:10:19.7271801Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:10:19.7272084Z         
2025-03-14T05:10:19.7272492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:10:19.7273460Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:10:19.7274157Z         
2025-03-14T05:10:19.7274565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:10:19.7275553Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:10:19.7276261Z         
2025-03-14T05:10:19.7276628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7277096Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7277349Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:19.7277581Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:19.7277853Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7278095Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:19.7278348Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:19.7278621Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7278867Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:19.7279099Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:19.7279364Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7279607Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:19.7279838Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:19.7280055Z         
2025-03-14T05:10:19.7280427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:19.7281186Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:19.7281953Z         
2025-03-14T05:10:19.7282415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:19.7282980Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:10:19.7283252Z         
2025-03-14T05:10:19.7283645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:19.7284225Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:19.7284531Z         
2025-03-14T05:10:19.7284927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:19.7285424Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:19.7285742Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7286069Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:19.7286341Z         
2025-03-14T05:10:19.7286736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:19.7287233Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:19.7287546Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:19.7287873Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:19.7288139Z         
2025-03-14T05:10:19.7288527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:19.7289001Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7289276Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:19.7289548Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:19.7289816Z         
2025-03-14T05:10:19.7290205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:19.7290707Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:19.7291005Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:19.7292035Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:19.7292299Z         
2025-03-14T05:10:19.7292706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:19.7293214Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:19.7293536Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:19.7293772Z         
2025-03-14T05:10:19.7294149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:19.7294645Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:19.7294962Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:19.7295187Z         
2025-03-14T05:10:19.7295566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:19.7296062Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:19.7296380Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:19.7296612Z         
2025-03-14T05:10:19.7297017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:19.7297555Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:19.7297892Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:19.7298129Z         
2025-03-14T05:10:19.7298546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:19.7299065Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:19.7299321Z         
2025-03-14T05:10:19.7299729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:19.7300238Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:19.7300494Z         
2025-03-14T05:10:19.7300917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:19.7301447Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:19.7301763Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:19.7302086Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:19.7302424Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:19.7302678Z         
2025-03-14T05:10:19.7303109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:19.7303655Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:19.7303973Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:19.7304382Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:19.7305778Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:19.7306079Z         
2025-03-14T05:10:19.7306524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:19.7307056Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:19.7307406Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:19.7307774Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:19.7308040Z         
2025-03-14T05:10:19.7308481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:19.7309010Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:19.7309359Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:19.7309722Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:19.7309986Z         
2025-03-14T05:10:19.7310403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:19.7310886Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:19.7311175Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:19.7311434Z         
2025-03-14T05:10:19.7311842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:19.7312311Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:19.7312582Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:19.7312821Z         
2025-03-14T05:10:19.7313222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:19.7313706Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:19.7314012Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:19.7314262Z         
2025-03-14T05:10:19.7314663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:19.7315146Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:19.7315446Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:19.7315702Z         
2025-03-14T05:10:19.7316145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:19.7316737Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:19.7317035Z         
2025-03-14T05:10:19.7317469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:19.7318035Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:19.7318318Z         
2025-03-14T05:10:19.7318751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:19.7319443Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:19.7319857Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:19.7320140Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:19.7320430Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:19.7320727Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:19.7320980Z         
2025-03-14T05:10:19.7321347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7321883Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7322221Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:19.7322464Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:19.7322820Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7323156Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:19.7323390Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:19.7323744Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7324076Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:19.7324323Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:19.7324690Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7325019Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:19.7325244Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:19.7325459Z         
2025-03-14T05:10:19.7325872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:19.7326412Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:10:19.7326691Z         
2025-03-14T05:10:19.7327123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:19.7327779Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:19.7328179Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:19.7328456Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:19.7328745Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:19.7329040Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:19.7329280Z         
2025-03-14T05:10:19.7329815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:19.7330511Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:19.7330844Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:19.7331172Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:19.7331502Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:19.7331818Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:19.7332057Z         
2025-03-14T05:10:19.7332490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:19.7332997Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:19.7333228Z         
2025-03-14T05:10:19.7351893Z 
2025-03-14T05:10:19.7357340Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:19.7361268Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:19.7364096Z         l_stack0_ = L_stack0_
2025-03-14T05:10:19.7364492Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:10:19.7364991Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:10:19.7366247Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:10:19.7366737Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:10:19.7367270Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:10:19.7367846Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:10:19.7368427Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:10:19.7369008Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:10:19.7369498Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7369911Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7370329Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7370729Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7371031Z         
2025-03-14T05:10:19.7371434Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:10:19.7371953Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:10:19.7372670Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:10:19.7373398Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:10:19.7374117Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:10:19.7374815Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:10:19.7375106Z         
2025-03-14T05:10:19.7375503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:10:19.7376431Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:10:19.7377111Z         
2025-03-14T05:10:19.7377512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:10:19.7378495Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:10:19.7379223Z         
2025-03-14T05:10:19.7379593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7380046Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7380297Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:19.7380527Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:19.7380796Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7381045Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:19.7381282Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:19.7381722Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7381973Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:19.7382213Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:19.7382490Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7382737Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:19.7382978Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:19.7383198Z         
2025-03-14T05:10:19.7383565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:19.7384425Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:19.7385086Z         
2025-03-14T05:10:19.7385582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:19.7386200Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:10:19.7386494Z         
2025-03-14T05:10:19.7386930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:19.7387496Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:19.7387796Z         
2025-03-14T05:10:19.7388220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:19.7388757Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:19.7389099Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7389447Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:19.7389732Z         
2025-03-14T05:10:19.7390160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:19.7390690Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:19.7391026Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:19.7391380Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:19.7391670Z         
2025-03-14T05:10:19.7392090Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:19.7392635Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7392966Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:19.7393257Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:19.7393520Z         
2025-03-14T05:10:19.7393947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:19.7394492Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:19.7394821Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:19.7395125Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:19.7395391Z         
2025-03-14T05:10:19.7395836Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:19.7396380Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:19.7396711Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:19.7396952Z         
2025-03-14T05:10:19.7397346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:19.7397857Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:19.7398177Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:19.7398413Z         
2025-03-14T05:10:19.7398800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:19.7399331Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:19.7399654Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:19.7399883Z         
2025-03-14T05:10:19.7400338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:19.7400874Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:19.7401218Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:19.7401452Z         
2025-03-14T05:10:19.7401879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:19.7402411Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:19.7402670Z         
2025-03-14T05:10:19.7403089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:19.7403610Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:19.7403868Z         
2025-03-14T05:10:19.7404302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:19.7404841Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:19.7405165Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:19.7405516Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:19.7405878Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:19.7406134Z         
2025-03-14T05:10:19.7406570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:19.7407111Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:19.7407431Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:19.7407760Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:19.7408099Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:19.7408360Z         
2025-03-14T05:10:19.7408785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:19.7409285Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:19.7409612Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:19.7409956Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:19.7410205Z         
2025-03-14T05:10:19.7410622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:19.7411120Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:19.7411486Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:19.7411840Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:19.7412095Z         
2025-03-14T05:10:19.7412511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:19.7412981Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:19.7413246Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:19.7413478Z         
2025-03-14T05:10:19.7413869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:19.7414317Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:19.7414575Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:19.7414810Z         
2025-03-14T05:10:19.7415196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:19.7415662Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:19.7415948Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:19.7416194Z         
2025-03-14T05:10:19.7416600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:19.7417068Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:19.7417353Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:19.7417599Z         
2025-03-14T05:10:19.7418049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:19.7418653Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:19.7418948Z         
2025-03-14T05:10:19.7419377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:19.7419935Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:19.7420226Z         
2025-03-14T05:10:19.7420679Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:19.7421357Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:19.7421786Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:19.7422077Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:19.7422374Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:19.7422680Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:19.7422936Z         
2025-03-14T05:10:19.7423312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7423863Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7424302Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:19.7424553Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:19.7424960Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7425313Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:19.7425562Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:19.7425933Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7426352Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:19.7426592Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:19.7426964Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7427319Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:19.7427572Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:19.7427800Z         
2025-03-14T05:10:19.7428231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:19.7428796Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:10:19.7429092Z         
2025-03-14T05:10:19.7429553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:19.7430706Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:19.7431135Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:19.7431423Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:19.7431718Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:19.7432030Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:19.7432297Z         
2025-03-14T05:10:19.7433653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:19.7434347Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:19.7434685Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:19.7435017Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:19.7435351Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:19.7435631Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:19.7435875Z         
2025-03-14T05:10:19.7436318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:19.7436840Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:19.7437075Z         
2025-03-14T05:10:19.7437212Z 
2025-03-14T05:10:19.7437315Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:19.7439199Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:19.7441217Z         l_stack0_ = L_stack0_
2025-03-14T05:10:19.7441566Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:10:19.7442049Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:10:19.7442529Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:10:19.7443005Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:10:19.7443536Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:10:19.7444106Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:10:19.7444681Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:10:19.7445248Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:10:19.7445726Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7446130Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7446548Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7446953Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:19.7447250Z         
2025-03-14T05:10:19.7447625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:10:19.7448620Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:10:19.7449327Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:10:19.7450040Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:10:19.7450757Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:10:19.7451476Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:10:19.7451764Z         
2025-03-14T05:10:19.7452162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:10:19.7453123Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:10:19.7453860Z         
2025-03-14T05:10:19.7454278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:10:19.7455301Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:10:19.7456035Z         
2025-03-14T05:10:19.7456414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7456966Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7457321Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:19.7457560Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:19.7457836Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7458090Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:19.7458322Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:19.7458598Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7458848Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:19.7459088Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:19.7459354Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:19.7459601Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:19.7459831Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:19.7460051Z         
2025-03-14T05:10:19.7460432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:19.7461241Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:19.7461834Z         
2025-03-14T05:10:19.7462303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:19.7462880Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:10:19.7463158Z         
2025-03-14T05:10:19.7463556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:19.7464089Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:19.7464436Z         
2025-03-14T05:10:19.7464860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:19.7465385Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:19.7465724Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7466074Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:19.7466371Z         
2025-03-14T05:10:19.7466775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:19.7467327Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:19.7467661Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:19.7468024Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:19.7468317Z         
2025-03-14T05:10:19.7468761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:19.7469283Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:19.7469588Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:19.7469880Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:19.7470148Z         
2025-03-14T05:10:19.7470569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:19.7471115Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:19.7471440Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:19.7471737Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:19.7472003Z         
2025-03-14T05:10:19.7472438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:19.7472978Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:19.7473321Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:19.7473570Z         
2025-03-14T05:10:19.7473977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:19.7474523Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:19.7474879Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:19.7475119Z         
2025-03-14T05:10:19.7475528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:19.7476067Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:19.7476404Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:19.7476651Z         
2025-03-14T05:10:19.7477073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:19.7477615Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:19.7477967Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:19.7478204Z         
2025-03-14T05:10:19.7478638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:19.7479178Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:19.7479437Z         
2025-03-14T05:10:19.7479858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:19.7480379Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:19.7480649Z         
2025-03-14T05:10:19.7481081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:19.7481781Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:19.7482113Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:19.7482509Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:19.7482857Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:19.7483117Z         
2025-03-14T05:10:19.7483552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:19.7484100Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:19.7484418Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:19.7484752Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:19.7485094Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:19.7485355Z         
2025-03-14T05:10:19.7485777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:19.7486283Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:19.7486619Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:19.7486967Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:19.7487219Z         
2025-03-14T05:10:19.7487670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:19.7488194Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:19.7488537Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:19.7488889Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:19.7489142Z         
2025-03-14T05:10:19.7489559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:19.7490026Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:19.7490296Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:19.7490531Z         
2025-03-14T05:10:19.7490927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:19.7491384Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:19.7491647Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:19.7491881Z         
2025-03-14T05:10:19.7492284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:19.7492774Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:19.7493082Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:19.7493331Z         
2025-03-14T05:10:19.7493735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:19.7494250Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:19.7494554Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:19.7494802Z         
2025-03-14T05:10:19.7495253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:19.7495841Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:19.7496138Z         
2025-03-14T05:10:19.7496562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:19.7497120Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:19.7497416Z         
2025-03-14T05:10:19.7497873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:19.7498552Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:19.7498976Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:19.7499261Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:19.7499558Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:19.7499865Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:19.7500119Z         
2025-03-14T05:10:19.7500504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:19.7501094Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7501461Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:19.7501708Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:19.7502079Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7502422Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:19.7502661Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:19.7503020Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7503358Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:19.7503595Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:19.7503958Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:19.7504368Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:19.7504614Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:19.7504843Z         
2025-03-14T05:10:19.7505295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:19.7505852Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:10:19.7506146Z         
2025-03-14T05:10:19.7506594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:19.7507289Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:19.7507710Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:19.7507993Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:19.7508286Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:19.7508613Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:19.7508858Z         
2025-03-14T05:10:19.7509405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:19.7510091Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:19.7510429Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:19.7510755Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:19.7511092Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:19.7511381Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:19.7511618Z         
2025-03-14T05:10:19.7512061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:19.7512581Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:19.7512823Z         
2025-03-14T05:10:20.2964858Z 
2025-03-14T05:10:20.2969210Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:20.2971166Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:20.2972085Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:10:20.2972328Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:10:20.2972664Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:20.2973110Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:20.2973512Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:20.2973909Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:20.2974209Z         
2025-03-14T05:10:20.2974637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.2975122Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.2975383Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:20.2975634Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:20.2975912Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.2976165Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:20.2976408Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:20.2976685Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.2976937Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:20.2977167Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:20.2977437Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.2977736Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:20.2977972Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:20.2978196Z         
2025-03-14T05:10:20.2978581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:20.2979417Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:20.2979977Z         
2025-03-14T05:10:20.2980449Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:20.2981033Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:10:20.2981310Z         
2025-03-14T05:10:20.2982240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:20.2982813Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:20.2983105Z         
2025-03-14T05:10:20.2983524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:20.2984040Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:20.2984441Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.2984779Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:20.2985065Z         
2025-03-14T05:10:20.2985535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:20.2986118Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:20.2986433Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:20.2986775Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:20.2987058Z         
2025-03-14T05:10:20.2987456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:20.2987952Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.2988236Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:20.2988513Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:20.2988764Z         
2025-03-14T05:10:20.2989164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:20.2989680Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:20.2989991Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:20.2990276Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:20.2990527Z         
2025-03-14T05:10:20.2990950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:20.2991459Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:20.2991810Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:20.2992048Z         
2025-03-14T05:10:20.2992447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:20.2992962Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:20.2993317Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:20.2993552Z         
2025-03-14T05:10:20.2993944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:20.2994451Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:20.2994775Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:20.2995012Z         
2025-03-14T05:10:20.2995406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:20.2995945Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:20.2996291Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:20.2996531Z         
2025-03-14T05:10:20.2996959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:20.2997510Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:20.2997775Z         
2025-03-14T05:10:20.2998208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:20.2998769Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:20.2999037Z         
2025-03-14T05:10:20.2999461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:20.3000000Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:20.3000324Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:20.3000658Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:20.3001010Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:20.3001271Z         
2025-03-14T05:10:20.3001706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:20.3002245Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:20.3002567Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:20.3002900Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:20.3003244Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:20.3003502Z         
2025-03-14T05:10:20.3003927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:20.3004438Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:20.3004791Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:20.3005142Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:20.3005427Z         
2025-03-14T05:10:20.3005869Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:20.3006369Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:20.3006707Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:20.3007054Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:20.3007310Z         
2025-03-14T05:10:20.3007715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:20.3008181Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:20.3008451Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:20.3008688Z         
2025-03-14T05:10:20.3009086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:20.3009540Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:20.3009816Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:20.3010649Z         
2025-03-14T05:10:20.3011065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:20.3011546Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:20.3011858Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:20.3012146Z         
2025-03-14T05:10:20.3012562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:20.3013038Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:20.3013336Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:20.3013588Z         
2025-03-14T05:10:20.3014020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:20.3014603Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:20.3014900Z         
2025-03-14T05:10:20.3015325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:20.3015885Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:20.3016172Z         
2025-03-14T05:10:20.3016620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:20.3017304Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:20.3017730Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:20.3018022Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:20.3018324Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:20.3018667Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:20.3018920Z         
2025-03-14T05:10:20.3019310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.3019913Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3020266Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:20.3020515Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:20.3020880Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3021223Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:20.3021465Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:20.3021826Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3022168Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:20.3022406Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:20.3022762Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3023100Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:20.3023331Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:20.3023581Z         
2025-03-14T05:10:20.3024003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:20.3024683Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:10:20.3025030Z         
2025-03-14T05:10:20.3025511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:20.3026221Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:20.3026654Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:20.3026941Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:20.3027234Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:20.3027536Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:20.3027787Z         
2025-03-14T05:10:20.3028348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:20.3029059Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:20.3029405Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:20.3029751Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:20.3030085Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:20.3030376Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:20.3030621Z         
2025-03-14T05:10:20.3031074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:20.3031606Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:20.3031876Z         
2025-03-14T05:10:20.3037296Z 
2025-03-14T05:10:20.3039792Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:20.3040902Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:20.3042016Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:10:20.3042260Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:10:20.3042586Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3043004Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3043422Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3043827Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3044128Z         
2025-03-14T05:10:20.3044565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.3045103Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3045473Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:20.3045723Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:20.3046012Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3046275Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:20.3046622Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:20.3051684Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3052171Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:20.3052679Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:20.3053151Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3053539Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:20.3054401Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:20.3054750Z         
2025-03-14T05:10:20.3055199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:20.3056001Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:20.3056566Z         
2025-03-14T05:10:20.3057049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:20.3057652Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:10:20.3057927Z         
2025-03-14T05:10:20.3058335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:20.3058878Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:20.3059164Z         
2025-03-14T05:10:20.3059568Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:20.3060071Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:20.3060568Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.3060903Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:20.3061183Z         
2025-03-14T05:10:20.3061597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:20.3062138Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:20.3062459Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:20.3062798Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:20.3063072Z         
2025-03-14T05:10:20.3063471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:20.3063977Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.3064427Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:20.3064723Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:20.3064991Z         
2025-03-14T05:10:20.3065429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:20.3065970Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:20.3066294Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:20.3066597Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:20.3066856Z         
2025-03-14T05:10:20.3067306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:20.3067851Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:20.3068184Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:20.3068426Z         
2025-03-14T05:10:20.3068822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:20.3069346Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:20.3069676Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:20.3069909Z         
2025-03-14T05:10:20.3070285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:20.3070784Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:20.3071103Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:20.3071335Z         
2025-03-14T05:10:20.3071719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:20.3072256Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:20.3072604Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:20.3072842Z         
2025-03-14T05:10:20.3073269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:20.3073830Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:20.3074094Z         
2025-03-14T05:10:20.3074513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:20.3075039Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:20.3075297Z         
2025-03-14T05:10:20.3075754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:20.3076299Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:20.3076625Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:20.3076959Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:20.3077315Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:20.3077573Z         
2025-03-14T05:10:20.3078013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:20.3078562Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:20.3078882Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:20.3079211Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:20.3079554Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:20.3079815Z         
2025-03-14T05:10:20.3080254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:20.3080776Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:20.3081108Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:20.3081639Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:20.3081913Z         
2025-03-14T05:10:20.3082342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:20.3082983Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:20.3083326Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:20.3083682Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:20.3083945Z         
2025-03-14T05:10:20.3084359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:20.3084831Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:20.3085107Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:20.3085348Z         
2025-03-14T05:10:20.3085776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:20.3086241Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:20.3086507Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:20.3086901Z         
2025-03-14T05:10:20.3087304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:20.3087796Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:20.3088099Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:20.3088359Z         
2025-03-14T05:10:20.3088799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:20.3089282Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:20.3089574Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:20.3089822Z         
2025-03-14T05:10:20.3090258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:20.3090853Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:20.3091150Z         
2025-03-14T05:10:20.3091580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:20.3092139Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:20.3092443Z         
2025-03-14T05:10:20.3092895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:20.3093575Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:20.3094009Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:20.3094331Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:20.3094659Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:20.3094974Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:20.3095238Z         
2025-03-14T05:10:20.3095625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.3096207Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3096568Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:20.3096826Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:20.3097206Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3097563Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:20.3097813Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:20.3098182Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3098531Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:20.3098777Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:20.3099148Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3099496Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:20.3099737Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:20.3099973Z         
2025-03-14T05:10:20.3100409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:20.3101035Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:10:20.3101366Z         
2025-03-14T05:10:20.3101814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:20.3102516Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:20.3102936Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:20.3103225Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:20.3103522Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:20.3103904Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:20.3104422Z         
2025-03-14T05:10:20.3105159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:20.3106004Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:20.3106523Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:20.3106899Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:20.3107273Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:20.3107594Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:20.3107865Z         
2025-03-14T05:10:20.3108403Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:20.3109017Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:20.3109283Z         
2025-03-14T05:10:20.3109495Z 
2025-03-14T05:10:20.3109637Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:20.3110694Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:10:20.3111570Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:10:20.3116666Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:10:20.3121150Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3123270Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3123810Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3130307Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:10:20.3130777Z         
2025-03-14T05:10:20.3131219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.3131720Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3131991Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:10:20.3132239Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:10:20.3132531Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3132935Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:10:20.3133199Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:10:20.3133499Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3133760Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:10:20.3134004Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:10:20.3134322Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:10:20.3134587Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:10:20.3134828Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:10:20.3135060Z         
2025-03-14T05:10:20.3135461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:10:20.3136260Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:10:20.3136835Z         
2025-03-14T05:10:20.3137301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:10:20.3137891Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:10:20.3138177Z         
2025-03-14T05:10:20.3138593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:10:20.3139128Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:10:20.3139422Z         
2025-03-14T05:10:20.3139838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:10:20.3140392Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:10:20.3141994Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.3142344Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:10:20.3142621Z         
2025-03-14T05:10:20.3143042Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:10:20.3143564Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:10:20.3143887Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:10:20.3144368Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:10:20.3144678Z         
2025-03-14T05:10:20.3145104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:10:20.3145623Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:10:20.3145925Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:10:20.3146224Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:10:20.3146486Z         
2025-03-14T05:10:20.3146901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:10:20.3147425Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:10:20.3147773Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:10:20.3148069Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:10:20.3148334Z         
2025-03-14T05:10:20.3148754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:10:20.3149308Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:10:20.3149646Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:10:20.3149890Z         
2025-03-14T05:10:20.3150291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:10:20.3150805Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:10:20.3151141Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:10:20.3151384Z         
2025-03-14T05:10:20.3151780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:10:20.3152300Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:10:20.3152628Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:10:20.3152867Z         
2025-03-14T05:10:20.3153260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:10:20.3153794Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:10:20.3154140Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:10:20.3154378Z         
2025-03-14T05:10:20.3154818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:10:20.3155372Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:10:20.3155629Z         
2025-03-14T05:10:20.3156055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:10:20.3156577Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:10:20.3156829Z         
2025-03-14T05:10:20.3157265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:10:20.3157812Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:10:20.3158137Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:10:20.3158483Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:10:20.3158839Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:10:20.3159104Z         
2025-03-14T05:10:20.3159542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:10:20.3160086Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:10:20.3160411Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:10:20.3160744Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:10:20.3161113Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:10:20.3161374Z         
2025-03-14T05:10:20.3161795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:10:20.3162321Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:10:20.3162654Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:10:20.3163004Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:10:20.3163257Z         
2025-03-14T05:10:20.3163674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:10:20.3164179Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:10:20.3164518Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:10:20.3164871Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:10:20.3165121Z         
2025-03-14T05:10:20.3165530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:10:20.3166001Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:10:20.3166275Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:10:20.3166510Z         
2025-03-14T05:10:20.3166898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:10:20.3167370Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:10:20.3167657Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:10:20.3167913Z         
2025-03-14T05:10:20.3168311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:10:20.3168798Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:10:20.3169095Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:10:20.3169348Z         
2025-03-14T05:10:20.3169743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:10:20.3170224Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:10:20.3170541Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:10:20.3170788Z         
2025-03-14T05:10:20.3171227Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:10:20.3171813Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:10:20.3172109Z         
2025-03-14T05:10:20.3172536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:10:20.3173097Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:10:20.3173390Z         
2025-03-14T05:10:20.3173842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:10:20.3174560Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:10:20.3174986Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:10:20.3175274Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:10:20.3175591Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:10:20.3175903Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:10:20.3176161Z         
2025-03-14T05:10:20.3176546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:10:20.3177097Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3177453Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:10:20.3177707Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:10:20.3178074Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3178418Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:10:20.3178663Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:10:20.3179026Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3179369Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:10:20.3179606Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:10:20.3179976Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:10:20.3180326Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:10:20.3180569Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:10:20.3180790Z         
2025-03-14T05:10:20.3181275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:10:20.3182165Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:10:20.3182511Z         
2025-03-14T05:10:20.3182964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:10:20.3183668Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:10:20.3184099Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:10:20.3184472Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:10:20.3184793Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:10:20.3185126Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:10:20.3185403Z         
2025-03-14T05:10:20.3186000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:20.3186720Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:10:20.3187079Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:20.3187429Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:10:20.3187872Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:20.3188182Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:20.3188439Z         
2025-03-14T05:10:20.3188900Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:20.3189443Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:20.3189731Z         
2025-03-14T05:10:20.6792212Z 
2025-03-14T05:10:20.6797923Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:20.6799715Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:10:20.6800110Z         l_scores_0_ = L_scores_0_
2025-03-14T05:10:20.6800330Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:10:20.6800536Z         
2025-03-14T05:10:20.6801183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:20.6801911Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:10:20.6802244Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:20.6802570Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:10:20.6802895Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:20.6803190Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:20.6803436Z         
2025-03-14T05:10:20.6803884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:20.6804417Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:20.6804660Z         
2025-03-14T05:10:20.6805066Z 
2025-03-14T05:10:20.6805225Z class GraphModule(torch.nn.Module):
2025-03-14T05:10:20.6805557Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:10:20.6805877Z         l_scores_0_ = L_scores_0_
2025-03-14T05:10:20.6806091Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:10:20.6806278Z         
2025-03-14T05:10:20.6806828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:10:20.6807466Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:10:20.6807777Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:10:20.6808085Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:10:20.6808388Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:10:20.6808671Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:10:20.6808904Z         
2025-03-14T05:10:20.6809338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:10:20.6809848Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:10:20.6810079Z         
2025-03-14T05:10:55.1495223Z Compilation time (from dynamo_timed): 55.520050256
2025-03-14T05:10:55.1497210Z pass
2025-03-14T05:10:55.1497800Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:10:55.1509295Z TIMING: entire_frame_compile:55.52005 gc:0.05177 _recursive_pre_grad_passes:0.04467 async_compile.wait:12.85466 backend_compile:31.83028 _recursive_joint_graph_passes:0.19638 inductor_compile:16.76833 _recursive_post_grad_passes:0.06168 code_gen:14.85 total_wall_time:55.52005
2025-03-14T05:10:55.1511992Z STATS: call_* op count: 1308 | FakeTensorMode.__torch_dispatch__:20210 | FakeTensor.__torch_dispatch__:738 | ProxyTorchDispatchMode.__torch_dispatch__:2202 | attempt fast:284 | slow no contiguity match:46 | fast is_contiguous:228 | slow both tensors nontrivially broadcast:10
2025-03-14T05:10:55.1545032Z Dynamo produced 78 graphs covering 1308 ops with 62 graph breaks (8 unique)
2025-03-14T05:11:01.7268130Z 
2025-03-14T05:11:15.4915020Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:11:15.4917853Z loading model: 0it [00:13, ?it/s]
2025-03-14T05:11:15.4918315Z cpu  eval  detectron2_maskrcnn_r_50_c4        
2025-03-14T05:11:23.0967048Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_50_c4. Setting accuracy check to cosine
2025-03-14T05:11:23.1036025Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:11:45.3886755Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:10.8396399Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:12:20.7225946Z 
2025-03-14T05:12:20.7226604Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:20.7270305Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:12:20.7313302Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:12:20.7313739Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7314396Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7315093Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7315805Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7316467Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7317094Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7317760Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7318488Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7319188Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7319870Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7320508Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7321169Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7321882Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7322610Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7323283Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7323946Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7324624Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7325348Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7326044Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7326716Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7327363Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.7328060Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7328804Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7329582Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.7330288Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.7330941Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7331611Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7332343Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7333051Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7333724Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7334362Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7335027Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7335765Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7336460Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7337152Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7337785Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7338450Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7339170Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7339863Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7340541Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7341176Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7341837Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7342575Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7343291Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7344002Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7344837Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7345639Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7346525Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7347376Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7348175Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7348910Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7349691Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7350571Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7351426Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7352222Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7352945Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7353677Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7354458Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7355155Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7355826Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7356463Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7357142Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7357894Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7358604Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7359283Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7359931Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7360613Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7361341Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7362050Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7362734Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7363404Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.7364137Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7364899Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7365632Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.7366330Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.7366988Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7367666Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7368397Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7369110Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7369795Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7370441Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7371121Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7371854Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7372550Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7373223Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7373866Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7374536Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7375261Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7375964Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7376654Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7377327Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7378000Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7378753Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7379469Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7380156Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7380810Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7381681Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7382431Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7383148Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7383844Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7384638Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7385391Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7386150Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7386867Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7387554Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7388215Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7388921Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7389673Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7390391Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7391081Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7391767Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7392461Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7393210Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7393923Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7394597Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7395253Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7395934Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7396673Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7397363Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7398028Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7398700Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7399382Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7400104Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7400799Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7401476Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7402128Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7402815Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7403550Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7404265Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7404978Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7405633Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7406335Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7407071Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7407787Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7408473Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7409123Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.7409815Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7410550Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7411274Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.7411989Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.7412653Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7413323Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7414041Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7414738Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7415414Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7416051Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7416715Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7417441Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7418142Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7418840Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7419496Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7420178Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7420919Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7421641Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7422330Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7422983Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7423688Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7424498Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7425302Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7426003Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7426655Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7427350Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7428083Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7428803Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7429499Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7430168Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7430858Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7431592Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7432343Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7433038Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7433729Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7434419Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7435161Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7435884Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7436577Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7437235Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7438291Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7439032Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7439827Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7440546Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7441199Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7441880Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7442607Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7443318Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7444002Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7444654Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7445372Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7446140Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7446896Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7447633Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7448302Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7448985Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7449722Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7450451Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7451186Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7451856Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7452537Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7453315Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7454083Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7454816Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7455501Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7456244Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7457090Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7458273Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7459311Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7460271Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7461300Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7462485Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7463707Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7464972Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7465817Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7466530Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7467304Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7468050Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7468781Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7469540Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:12:20.7470311Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:12:20.7471065Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:12:20.7471863Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:12:20.7472694Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:12:20.7473507Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:12:20.7474307Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:12:20.7474805Z         
2025-03-14T05:12:20.7475218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7476134Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7476774Z         
2025-03-14T05:12:20.7477172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7479078Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7480730Z         
2025-03-14T05:12:20.7481136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:12:20.7481766Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:12:20.7482055Z         
2025-03-14T05:12:20.7482546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:12:20.7483243Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:12:20.7483620Z         
2025-03-14T05:12:20.7483990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7484781Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7485353Z         
2025-03-14T05:12:20.7485805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7487790Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7489532Z         
2025-03-14T05:12:20.7489930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7490446Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:12:20.7490726Z         
2025-03-14T05:12:20.7491091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7491876Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7492447Z         
2025-03-14T05:12:20.7492817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7494827Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7496523Z         
2025-03-14T05:12:20.7496917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7497430Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:12:20.7497702Z         
2025-03-14T05:12:20.7498062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7498850Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7499441Z         
2025-03-14T05:12:20.7499812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7501775Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7503502Z         
2025-03-14T05:12:20.7503864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7504773Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.7505403Z         
2025-03-14T05:12:20.7505777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7507886Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7509808Z         
2025-03-14T05:12:20.7510222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7510760Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:12:20.7511054Z         
2025-03-14T05:12:20.7511469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7512018Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:12:20.7512320Z         
2025-03-14T05:12:20.7512699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7513531Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7514101Z         
2025-03-14T05:12:20.7514470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7516426Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7518145Z         
2025-03-14T05:12:20.7518541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7519055Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:12:20.7519329Z         
2025-03-14T05:12:20.7519686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7520463Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7521035Z         
2025-03-14T05:12:20.7521407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7523338Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7525041Z         
2025-03-14T05:12:20.7525410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7525886Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:12:20.7526146Z         
2025-03-14T05:12:20.7526482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7527228Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7527801Z         
2025-03-14T05:12:20.7528175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7530085Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7531779Z         
2025-03-14T05:12:20.7532158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7532662Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:12:20.7532945Z         
2025-03-14T05:12:20.7533322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7533823Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:12:20.7534102Z         
2025-03-14T05:12:20.7534453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7535203Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7535756Z         
2025-03-14T05:12:20.7536119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7538079Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7539819Z         
2025-03-14T05:12:20.7540213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7540725Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:12:20.7541007Z         
2025-03-14T05:12:20.7541364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7542139Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7542711Z         
2025-03-14T05:12:20.7543082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7545195Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7546912Z         
2025-03-14T05:12:20.7547290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7547789Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:12:20.7548055Z         
2025-03-14T05:12:20.7548397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7549142Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7549696Z         
2025-03-14T05:12:20.7550052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7551902Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7553547Z         
2025-03-14T05:12:20.7553917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7554406Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:12:20.7554680Z         
2025-03-14T05:12:20.7555052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7555550Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:12:20.7555823Z         
2025-03-14T05:12:20.7556166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7556906Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7557452Z         
2025-03-14T05:12:20.7557805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7559674Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7561324Z         
2025-03-14T05:12:20.7561704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7562194Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:12:20.7562464Z         
2025-03-14T05:12:20.7562810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7563555Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7564109Z         
2025-03-14T05:12:20.7564464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7566358Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7567989Z         
2025-03-14T05:12:20.7568364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7568850Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:12:20.7569115Z         
2025-03-14T05:12:20.7569450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7570192Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7570737Z         
2025-03-14T05:12:20.7571089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7572933Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7574564Z         
2025-03-14T05:12:20.7574905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7575656Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.7576216Z         
2025-03-14T05:12:20.7576567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7578439Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7580208Z         
2025-03-14T05:12:20.7580616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7581128Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:12:20.7581410Z         
2025-03-14T05:12:20.7581944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7582477Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:12:20.7582771Z         
2025-03-14T05:12:20.7583133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7583919Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7584574Z         
2025-03-14T05:12:20.7584976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7587043Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7588784Z         
2025-03-14T05:12:20.7589179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7589688Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:12:20.7589967Z         
2025-03-14T05:12:20.7590329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7591118Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7591697Z         
2025-03-14T05:12:20.7592068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7593942Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7595624Z         
2025-03-14T05:12:20.7596005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7596496Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:12:20.7596754Z         
2025-03-14T05:12:20.7597093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7597833Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7598389Z         
2025-03-14T05:12:20.7598732Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7600621Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7602259Z         
2025-03-14T05:12:20.7602624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7603125Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:12:20.7603401Z         
2025-03-14T05:12:20.7603773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7604270Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:12:20.7604545Z         
2025-03-14T05:12:20.7604884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7605624Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7606166Z         
2025-03-14T05:12:20.7606520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7608405Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7610015Z         
2025-03-14T05:12:20.7610392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7610874Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:12:20.7611142Z         
2025-03-14T05:12:20.7611483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7612220Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7612769Z         
2025-03-14T05:12:20.7613121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7614991Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7616619Z         
2025-03-14T05:12:20.7616991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7617476Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:12:20.7617742Z         
2025-03-14T05:12:20.7618083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7618824Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7619374Z         
2025-03-14T05:12:20.7619727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7621582Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7623219Z         
2025-03-14T05:12:20.7623593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7624109Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:12:20.7624471Z         
2025-03-14T05:12:20.7624878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7625396Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:12:20.7625674Z         
2025-03-14T05:12:20.7626022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7626766Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7627321Z         
2025-03-14T05:12:20.7627676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7629542Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7631178Z         
2025-03-14T05:12:20.7631555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7632039Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:12:20.7632303Z         
2025-03-14T05:12:20.7632642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7633390Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7633919Z         
2025-03-14T05:12:20.7634267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7636130Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7637751Z         
2025-03-14T05:12:20.7638125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7638616Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:12:20.7638885Z         
2025-03-14T05:12:20.7639222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7639959Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7640508Z         
2025-03-14T05:12:20.7640858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7642732Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7644381Z         
2025-03-14T05:12:20.7644748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7645242Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:12:20.7645519Z         
2025-03-14T05:12:20.7645887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7646379Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:12:20.7646655Z         
2025-03-14T05:12:20.7646984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7647719Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7648283Z         
2025-03-14T05:12:20.7648636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7650500Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7652106Z         
2025-03-14T05:12:20.7652485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7652969Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:12:20.7653233Z         
2025-03-14T05:12:20.7653584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7654300Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7654831Z         
2025-03-14T05:12:20.7655176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7656971Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7658542Z         
2025-03-14T05:12:20.7658907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7659373Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:12:20.7659656Z         
2025-03-14T05:12:20.7659986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7660694Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7661217Z         
2025-03-14T05:12:20.7661560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7663455Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7665196Z         
2025-03-14T05:12:20.7665556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7666299Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.7666849Z         
2025-03-14T05:12:20.7667206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7669122Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7671215Z         
2025-03-14T05:12:20.7671618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7672108Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:12:20.7672372Z         
2025-03-14T05:12:20.7672756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7673260Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:12:20.7673533Z         
2025-03-14T05:12:20.7673879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7674781Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7675320Z         
2025-03-14T05:12:20.7675673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7677695Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7679313Z         
2025-03-14T05:12:20.7679685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7680168Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:12:20.7680431Z         
2025-03-14T05:12:20.7680771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7681606Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7682159Z         
2025-03-14T05:12:20.7682517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7684380Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7686013Z         
2025-03-14T05:12:20.7686392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7686871Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:12:20.7687135Z         
2025-03-14T05:12:20.7687476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7688209Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7688751Z         
2025-03-14T05:12:20.7689106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7690964Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7692593Z         
2025-03-14T05:12:20.7692962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7693439Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:12:20.7693706Z         
2025-03-14T05:12:20.7694077Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7694560Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:12:20.7694825Z         
2025-03-14T05:12:20.7695163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7695885Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7696416Z         
2025-03-14T05:12:20.7696769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7698649Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7700289Z         
2025-03-14T05:12:20.7700678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7701192Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:12:20.7701463Z         
2025-03-14T05:12:20.7701815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7702610Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7703219Z         
2025-03-14T05:12:20.7703607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7705692Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7707486Z         
2025-03-14T05:12:20.7707910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7708443Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:12:20.7708731Z         
2025-03-14T05:12:20.7709111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7709914Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7710486Z         
2025-03-14T05:12:20.7710856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7712804Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7714521Z         
2025-03-14T05:12:20.7714907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7715417Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:12:20.7715698Z         
2025-03-14T05:12:20.7716088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7716596Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:12:20.7716871Z         
2025-03-14T05:12:20.7717210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7717935Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7718473Z         
2025-03-14T05:12:20.7718831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7720675Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7722275Z         
2025-03-14T05:12:20.7722655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7723162Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:12:20.7723437Z         
2025-03-14T05:12:20.7723797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7724567Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7725116Z         
2025-03-14T05:12:20.7725475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7727305Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7728951Z         
2025-03-14T05:12:20.7729325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7729803Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:12:20.7730063Z         
2025-03-14T05:12:20.7730399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7731140Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7731683Z         
2025-03-14T05:12:20.7732035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7733870Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7735576Z         
2025-03-14T05:12:20.7735964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7736475Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:12:20.7736754Z         
2025-03-14T05:12:20.7737153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7737670Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:12:20.7737946Z         
2025-03-14T05:12:20.7738304Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7739074Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7739635Z         
2025-03-14T05:12:20.7740009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7741951Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7743691Z         
2025-03-14T05:12:20.7744099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7744719Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:12:20.7745006Z         
2025-03-14T05:12:20.7745384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7746200Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7746770Z         
2025-03-14T05:12:20.7747142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7749152Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7750841Z         
2025-03-14T05:12:20.7751236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7751740Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:12:20.7752011Z         
2025-03-14T05:12:20.7752367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7753138Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7753703Z         
2025-03-14T05:12:20.7754073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7756019Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7757764Z         
2025-03-14T05:12:20.7758156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7758671Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:12:20.7758956Z         
2025-03-14T05:12:20.7759327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7759811Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:12:20.7760075Z         
2025-03-14T05:12:20.7760409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7761143Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.7761693Z         
2025-03-14T05:12:20.7762047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7763920Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7765615Z         
2025-03-14T05:12:20.7766016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7766524Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:12:20.7766804Z         
2025-03-14T05:12:20.7767145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7767872Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.7768409Z         
2025-03-14T05:12:20.7768795Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7770639Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7772263Z         
2025-03-14T05:12:20.7772640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7773121Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:12:20.7773378Z         
2025-03-14T05:12:20.7773719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7774464Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.7775036Z         
2025-03-14T05:12:20.7775409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.7777381Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.7779089Z         
2025-03-14T05:12:20.7779481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.7779992Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:12:20.7780273Z         
2025-03-14T05:12:20.7780656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.7781166Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:12:20.7781726Z         
2025-03-14T05:12:20.7782299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.7782975Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.7783268Z         
2025-03-14T05:12:20.7783848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.7802175Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:20.7802484Z         
2025-03-14T05:12:20.7803091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.7803767Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.7804047Z         
2025-03-14T05:12:20.7804450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.7804955Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:12:20.7805219Z         
2025-03-14T05:12:20.7805690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:12:20.7806302Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:12:20.7806645Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:12:20.7806921Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:12:20.7807165Z         
2025-03-14T05:12:20.7807585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:12:20.7808100Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:12:20.7808511Z         
2025-03-14T05:12:20.7808932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:12:20.7809452Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:12:20.7809691Z         
2025-03-14T05:12:20.7810208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:12:20.7810855Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:12:20.7811186Z         
2025-03-14T05:12:20.7811693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:12:20.7812286Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:12:20.7812895Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:12:20.7813504Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:12:20.7813801Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:12:20.7814037Z         
2025-03-14T05:12:20.7814426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.7814906Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:12:20.7815162Z         
2025-03-14T05:12:20.7815513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.7816629Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:12:20.7817545Z         
2025-03-14T05:12:20.7817904Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:12:20.7818432Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:12:20.7818730Z         
2025-03-14T05:12:20.7819200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:12:20.7820493Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:12:20.7821458Z         
2025-03-14T05:12:20.7821906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:12:20.7823150Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:12:20.7824092Z         
2025-03-14T05:12:20.7824627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:12:20.7825185Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:12:20.7825537Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:12:20.7825812Z         
2025-03-14T05:12:20.7826325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:12:20.7826950Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:12:20.7827332Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:12:20.7827729Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:12:20.7828024Z         
2025-03-14T05:12:20.7828508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:20.7829165Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:12:20.7829487Z         
2025-03-14T05:12:20.7830020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:20.7830667Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:12:20.7831022Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:20.7831359Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:20.7831617Z         
2025-03-14T05:12:20.7832080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:20.7832681Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:12:20.7832977Z         
2025-03-14T05:12:20.7833377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:20.7833886Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:20.7834150Z         
2025-03-14T05:12:20.7834560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:20.7835062Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:20.7835376Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.7835704Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:20.7835992Z         
2025-03-14T05:12:20.7836397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:20.7836892Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:20.7837194Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:20.7837535Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:20.7837805Z         
2025-03-14T05:12:20.7838203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:20.7838689Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.7838960Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:20.7839226Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:12:20.7839473Z         
2025-03-14T05:12:20.7839876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:20.7840394Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:20.7840688Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:20.7840968Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:12:20.7841211Z         
2025-03-14T05:12:20.7841640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:20.7842163Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:20.7842495Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:20.7842734Z         
2025-03-14T05:12:20.7843145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:20.7844345Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:20.7844667Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:20.7844903Z         
2025-03-14T05:12:20.7845300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:20.7845806Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:20.7846130Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:12:20.7846366Z         
2025-03-14T05:12:20.7846760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:20.7847300Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:20.7847649Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:12:20.7847887Z         
2025-03-14T05:12:20.7848316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:20.7848853Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:20.7849123Z         
2025-03-14T05:12:20.7849534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:20.7850083Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:20.7850342Z         
2025-03-14T05:12:20.7850776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:20.7851325Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:20.7851680Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:12:20.7852012Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:20.7852359Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:12:20.7852622Z         
2025-03-14T05:12:20.7853050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:20.7853586Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:20.7853902Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:12:20.7854227Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:20.7854579Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:12:20.7854844Z         
2025-03-14T05:12:20.7855258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:20.7855754Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:20.7856077Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:20.7856466Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:12:20.7856757Z         
2025-03-14T05:12:20.7857184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:20.7857695Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:20.7858037Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:20.7858390Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:12:20.7858649Z         
2025-03-14T05:12:20.7859057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:20.7859538Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:20.7859815Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:20.7860064Z         
2025-03-14T05:12:20.7860485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:20.7860969Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:20.7861240Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:20.7861472Z         
2025-03-14T05:12:20.7861899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:20.7862442Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:20.7862746Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:20.7863025Z         
2025-03-14T05:12:20.7863435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:20.7863931Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:20.7864372Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:20.7864639Z         
2025-03-14T05:12:20.7865111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:20.7865709Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:20.7866015Z         
2025-03-14T05:12:20.7866451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:20.7867015Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:20.7867307Z         
2025-03-14T05:12:20.7867791Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:20.7868410Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:20.7868716Z         
2025-03-14T05:12:20.7869299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:20.7870006Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:20.7870263Z         
2025-03-14T05:12:20.7870665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.7871176Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:12:20.7871449Z         
2025-03-14T05:12:20.7871976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:20.7872582Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:12:20.7872866Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:20.7873146Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:20.7873390Z         
2025-03-14T05:12:20.7873942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:20.7874626Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:20.7875083Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:12:20.7875438Z         
2025-03-14T05:12:20.7875983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:20.7876657Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:20.7876953Z         
2025-03-14T05:12:20.7877341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.7877869Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:20.7878147Z         
2025-03-14T05:12:20.7878616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:20.7879217Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:20.7879487Z         
2025-03-14T05:12:20.7879875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.7880368Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:12:20.7880633Z         
2025-03-14T05:12:20.7881103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:20.7881932Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:20.7882198Z         
2025-03-14T05:12:20.7882777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:20.7883467Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:12:20.7883787Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:20.7884126Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:20.7884475Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:20.7884737Z         
2025-03-14T05:12:20.7885278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:20.7885837Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:20.7886079Z         
2025-03-14T05:12:20.7886471Z 
2025-03-14T05:12:20.7886573Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:20.7928228Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:12:20.7968855Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:12:20.7969382Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7970204Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7971049Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7971754Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7972414Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7973062Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7973759Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7974511Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7975283Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7975977Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7976647Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7977344Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7978089Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7978813Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7979509Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7980165Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7980853Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7981719Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7982465Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7983180Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7983854Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.7984636Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7985456Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7986224Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.7986948Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.7987614Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7988299Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7989117Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7989849Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.7990571Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.7991243Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.7991933Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7992689Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7993404Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.7994111Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.7994781Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.7995486Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7996237Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.7996949Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.7997628Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.7998271Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.7998942Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.7999670Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8000377Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8001054Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8001696Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8002362Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8003094Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8003805Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8004472Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8005127Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8005789Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8006505Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8007200Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8007868Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8008505Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8009182Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8009922Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8010615Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8011279Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8011925Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8012603Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8013327Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8014018Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8014684Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8015318Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8015999Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8016713Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8017429Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8018098Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8018763Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.8019483Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8020247Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8020986Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.8021701Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.8022359Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8023058Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8023810Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8024619Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8025349Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8026001Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8026686Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8027428Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8028143Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8028836Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8029514Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8030199Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8030945Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8031680Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8032369Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8033026Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8033718Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8034462Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8035181Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8035879Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8036559Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8037257Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8038005Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8038718Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8039400Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8040053Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8040743Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8041479Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8042190Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8042876Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8043552Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8044234Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8044986Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8045694Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8046383Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8047037Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8047723Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8048453Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8049163Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8049863Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8050540Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8051233Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8051981Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8052702Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8053407Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8054072Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8054768Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8055512Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8056236Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8056948Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8057610Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8058327Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8059073Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8059788Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8060482Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8061137Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8061828Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8062566Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8063286Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8064000Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8064830Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.8065664Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8066545Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8067373Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.8068142Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.8068868Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8069605Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8070396Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8071166Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8071939Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8072664Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8073399Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8074191Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8074959Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8075686Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8076390Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8077124Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8077884Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8078605Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8079308Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8079959Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8080645Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8081378Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8082231Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8082921Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8083575Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8084258Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8085009Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8085773Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8086458Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8087127Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8087807Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8088546Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8089265Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8089954Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8090609Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8091294Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8092027Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8092780Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8093523Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8094171Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8094849Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8095575Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8095899Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8096215Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8096507Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8096843Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8097209Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8097538Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8097869Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8098163Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8098501Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8098843Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8099160Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8099480Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8099765Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8100112Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8100472Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8100806Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8101126Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8101406Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8101751Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8102088Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8102414Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8102726Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8103017Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8103363Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8103717Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8104074Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8104560Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8104893Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8105279Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8105639Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8105972Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8106329Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8106654Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8107044Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8107442Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8107813Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8108169Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8108559Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:12:20.8108927Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:12:20.8109277Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:12:20.8109701Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:12:20.8110110Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:12:20.8110500Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:12:20.8110907Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:12:20.8110988Z         
2025-03-14T05:12:20.8111320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8111886Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8111965Z         
2025-03-14T05:12:20.8112245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8113708Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8113789Z         
2025-03-14T05:12:20.8114080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:12:20.8114235Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:12:20.8114303Z         
2025-03-14T05:12:20.8114686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:12:20.8114949Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:12:20.8115026Z         
2025-03-14T05:12:20.8115285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8115709Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8115775Z         
2025-03-14T05:12:20.8116054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8117581Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8117663Z         
2025-03-14T05:12:20.8117962Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8118102Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:12:20.8118176Z         
2025-03-14T05:12:20.8118445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8118875Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8118941Z         
2025-03-14T05:12:20.8119216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8120769Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8120838Z         
2025-03-14T05:12:20.8121148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8121300Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:12:20.8121375Z         
2025-03-14T05:12:20.8121628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8122071Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8122137Z         
2025-03-14T05:12:20.8122410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8123939Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8124006Z         
2025-03-14T05:12:20.8124282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8124720Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8124796Z         
2025-03-14T05:12:20.8125074Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8126640Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8126716Z         
2025-03-14T05:12:20.8126992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8127148Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:12:20.8127213Z         
2025-03-14T05:12:20.8127508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8127676Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:12:20.8127767Z         
2025-03-14T05:12:20.8128023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8128437Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8128503Z         
2025-03-14T05:12:20.8128769Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8130226Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8130295Z         
2025-03-14T05:12:20.8130581Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8130743Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:12:20.8130818Z         
2025-03-14T05:12:20.8131064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8131502Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8131575Z         
2025-03-14T05:12:20.8131832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8133318Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8133384Z         
2025-03-14T05:12:20.8133669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8133807Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:12:20.8133881Z         
2025-03-14T05:12:20.8134137Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8134585Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8134657Z         
2025-03-14T05:12:20.8134922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8136443Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8136511Z         
2025-03-14T05:12:20.8136804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8136962Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:12:20.8137051Z         
2025-03-14T05:12:20.8137331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8137488Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:12:20.8137552Z         
2025-03-14T05:12:20.8137825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8138249Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8138316Z         
2025-03-14T05:12:20.8138594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8140102Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8140176Z         
2025-03-14T05:12:20.8140475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8140631Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:12:20.8140723Z         
2025-03-14T05:12:20.8140972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8141407Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8141473Z         
2025-03-14T05:12:20.8141745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8143259Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8143336Z         
2025-03-14T05:12:20.8143629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8143785Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:12:20.8143862Z         
2025-03-14T05:12:20.8144114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8144654Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8144729Z         
2025-03-14T05:12:20.8145018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8146582Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8146659Z         
2025-03-14T05:12:20.8146952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8147111Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:12:20.8147187Z         
2025-03-14T05:12:20.8147494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8147672Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:12:20.8147739Z         
2025-03-14T05:12:20.8148009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8148425Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8148497Z         
2025-03-14T05:12:20.8148760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8150282Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8150370Z         
2025-03-14T05:12:20.8150660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8150812Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:12:20.8150879Z         
2025-03-14T05:12:20.8151159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8151585Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8151659Z         
2025-03-14T05:12:20.8151920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8153422Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8153496Z         
2025-03-14T05:12:20.8153797Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8153972Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:12:20.8154038Z         
2025-03-14T05:12:20.8154295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8154719Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8154795Z         
2025-03-14T05:12:20.8155056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8156603Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8156679Z         
2025-03-14T05:12:20.8156927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8157388Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8157456Z         
2025-03-14T05:12:20.8157742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8159299Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8159378Z         
2025-03-14T05:12:20.8159655Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8159798Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:12:20.8159870Z         
2025-03-14T05:12:20.8160141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8160314Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:12:20.8160391Z         
2025-03-14T05:12:20.8160639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8161046Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8161118Z         
2025-03-14T05:12:20.8161371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8162842Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8162917Z         
2025-03-14T05:12:20.8163198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8163373Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:12:20.8163435Z         
2025-03-14T05:12:20.8163688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8164118Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8164194Z         
2025-03-14T05:12:20.8164460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8165985Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8166064Z         
2025-03-14T05:12:20.8166354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8166504Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:12:20.8166573Z         
2025-03-14T05:12:20.8166852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8167289Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8167365Z         
2025-03-14T05:12:20.8167627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8169136Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8169214Z         
2025-03-14T05:12:20.8169492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8169659Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:12:20.8169743Z         
2025-03-14T05:12:20.8170037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8170190Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:12:20.8170266Z         
2025-03-14T05:12:20.8170518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8170961Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8171037Z         
2025-03-14T05:12:20.8171301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8172802Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8172871Z         
2025-03-14T05:12:20.8173161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8173318Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:12:20.8173407Z         
2025-03-14T05:12:20.8173656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8174087Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8174162Z         
2025-03-14T05:12:20.8174421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8175923Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8175990Z         
2025-03-14T05:12:20.8176279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8176437Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:12:20.8176512Z         
2025-03-14T05:12:20.8176761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8177211Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8177283Z         
2025-03-14T05:12:20.8177545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8179054Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8179122Z         
2025-03-14T05:12:20.8179409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8179561Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:12:20.8179638Z         
2025-03-14T05:12:20.8179940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8180112Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:12:20.8180186Z         
2025-03-14T05:12:20.8180440Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8180866Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8180931Z         
2025-03-14T05:12:20.8181205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8182963Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8183104Z         
2025-03-14T05:12:20.8183422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8183572Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:12:20.8183651Z         
2025-03-14T05:12:20.8183944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8184455Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8184530Z         
2025-03-14T05:12:20.8184815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8186391Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8186468Z         
2025-03-14T05:12:20.8186761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8186933Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:12:20.8187029Z         
2025-03-14T05:12:20.8187279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8187717Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8187783Z         
2025-03-14T05:12:20.8188051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8189565Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8189639Z         
2025-03-14T05:12:20.8189925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8190103Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:12:20.8190178Z         
2025-03-14T05:12:20.8190461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8190633Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:12:20.8190700Z         
2025-03-14T05:12:20.8190955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8191370Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8191446Z         
2025-03-14T05:12:20.8191712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8193217Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8193306Z         
2025-03-14T05:12:20.8193604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8193751Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:12:20.8193816Z         
2025-03-14T05:12:20.8194072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8194489Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8194564Z         
2025-03-14T05:12:20.8194825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8196328Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8196416Z         
2025-03-14T05:12:20.8196707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8196853Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:12:20.8196919Z         
2025-03-14T05:12:20.8197193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8197616Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8197690Z         
2025-03-14T05:12:20.8197950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8199477Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8199553Z         
2025-03-14T05:12:20.8199803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8200258Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8200338Z         
2025-03-14T05:12:20.8200622Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8202196Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8202265Z         
2025-03-14T05:12:20.8202549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8202688Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:12:20.8202761Z         
2025-03-14T05:12:20.8203040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8203209Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:12:20.8203273Z         
2025-03-14T05:12:20.8203530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8203975Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8204049Z         
2025-03-14T05:12:20.8204310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8205786Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8205859Z         
2025-03-14T05:12:20.8206135Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8206274Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:12:20.8206353Z         
2025-03-14T05:12:20.8206616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8207024Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8207096Z         
2025-03-14T05:12:20.8207353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8208821Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8208895Z         
2025-03-14T05:12:20.8209173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8209314Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:12:20.8209438Z         
2025-03-14T05:12:20.8209687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8210104Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8210177Z         
2025-03-14T05:12:20.8210432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8211905Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8211979Z         
2025-03-14T05:12:20.8212251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8212406Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:12:20.8212472Z         
2025-03-14T05:12:20.8212770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8212922Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:12:20.8212998Z         
2025-03-14T05:12:20.8213242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8213660Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8213726Z         
2025-03-14T05:12:20.8213997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8215501Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8215566Z         
2025-03-14T05:12:20.8215854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8216009Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:12:20.8216084Z         
2025-03-14T05:12:20.8216333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8216771Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8216845Z         
2025-03-14T05:12:20.8217105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8218612Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8218681Z         
2025-03-14T05:12:20.8218973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8219105Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:12:20.8219195Z         
2025-03-14T05:12:20.8219465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8219895Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8219968Z         
2025-03-14T05:12:20.8220230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8221725Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8221793Z         
2025-03-14T05:12:20.8222088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8222235Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:12:20.8222327Z         
2025-03-14T05:12:20.8222616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8222769Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:12:20.8222835Z         
2025-03-14T05:12:20.8223115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8223548Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8223615Z         
2025-03-14T05:12:20.8223896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8225553Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8225640Z         
2025-03-14T05:12:20.8225955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8226118Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:12:20.8226193Z         
2025-03-14T05:12:20.8226452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8226888Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8226955Z         
2025-03-14T05:12:20.8227239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8228827Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8228907Z         
2025-03-14T05:12:20.8229213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8229376Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:12:20.8229452Z         
2025-03-14T05:12:20.8229712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8230176Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8230244Z         
2025-03-14T05:12:20.8230521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8232083Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8232161Z         
2025-03-14T05:12:20.8232451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8232630Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:12:20.8232721Z         
2025-03-14T05:12:20.8233010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8233160Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:12:20.8233229Z         
2025-03-14T05:12:20.8233493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8233909Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8233986Z         
2025-03-14T05:12:20.8234263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8235754Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8235842Z         
2025-03-14T05:12:20.8236120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8236257Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:12:20.8236321Z         
2025-03-14T05:12:20.8236587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8236991Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8237065Z         
2025-03-14T05:12:20.8237326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8238820Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8238895Z         
2025-03-14T05:12:20.8239195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8239348Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:12:20.8239415Z         
2025-03-14T05:12:20.8239676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8240103Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8240177Z         
2025-03-14T05:12:20.8240444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8241932Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8242003Z         
2025-03-14T05:12:20.8242277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8242445Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:12:20.8242509Z         
2025-03-14T05:12:20.8242786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8242940Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:12:20.8243014Z         
2025-03-14T05:12:20.8243262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8243667Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8243734Z         
2025-03-14T05:12:20.8244003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8245511Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8245592Z         
2025-03-14T05:12:20.8245901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8246033Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:12:20.8246106Z         
2025-03-14T05:12:20.8246351Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8246775Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8246839Z         
2025-03-14T05:12:20.8247110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8248629Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8248709Z         
2025-03-14T05:12:20.8249001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8249137Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:12:20.8249210Z         
2025-03-14T05:12:20.8249473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8249896Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8249961Z         
2025-03-14T05:12:20.8250229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8251725Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8251795Z         
2025-03-14T05:12:20.8252093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8252262Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:12:20.8252335Z         
2025-03-14T05:12:20.8252625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8252775Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:12:20.8252840Z         
2025-03-14T05:12:20.8253286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.8253439Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.8253515Z         
2025-03-14T05:12:20.8253815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8253964Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:20.8254029Z         
2025-03-14T05:12:20.8254471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.8254621Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.8254694Z         
2025-03-14T05:12:20.8254988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8255150Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:12:20.8255221Z         
2025-03-14T05:12:20.8255600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:12:20.8255788Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:12:20.8255902Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:12:20.8256034Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:12:20.8256111Z         
2025-03-14T05:12:20.8256446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:12:20.8256572Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:12:20.8256646Z         
2025-03-14T05:12:20.8256970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:12:20.8257101Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:12:20.8257167Z         
2025-03-14T05:12:20.8257556Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:12:20.8257767Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:12:20.8257839Z         
2025-03-14T05:12:20.8258274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:12:20.8258426Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:12:20.8258873Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:12:20.8259009Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:12:20.8259126Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:12:20.8259200Z         
2025-03-14T05:12:20.8259503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.8259640Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:12:20.8259711Z         
2025-03-14T05:12:20.8259980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8260771Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:12:20.8260848Z         
2025-03-14T05:12:20.8261128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:12:20.8261324Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:12:20.8261416Z         
2025-03-14T05:12:20.8261807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:12:20.8262698Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:12:20.8262765Z         
2025-03-14T05:12:20.8263140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:12:20.8263975Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:12:20.8264051Z         
2025-03-14T05:12:20.8264463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:12:20.8264622Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:12:20.8264770Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:12:20.8264842Z         
2025-03-14T05:12:20.8265291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:12:20.8265465Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:12:20.8265654Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:12:20.8265834Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:12:20.8265922Z         
2025-03-14T05:12:20.8266317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:20.8266531Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:12:20.8266599Z         
2025-03-14T05:12:20.8267033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:20.8267180Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:12:20.8267332Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:20.8267467Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:20.8267540Z         
2025-03-14T05:12:20.8267907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:20.8268103Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:12:20.8268168Z         
2025-03-14T05:12:20.8268483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:20.8268623Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:20.8268714Z         
2025-03-14T05:12:20.8269028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:20.8269168Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:20.8269294Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.8269445Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:20.8269511Z         
2025-03-14T05:12:20.8269838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:20.8269965Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:20.8270094Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:20.8270240Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:20.8270316Z         
2025-03-14T05:12:20.8270624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:20.8270754Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.8270844Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:20.8270974Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:12:20.8271052Z         
2025-03-14T05:12:20.8271385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:20.8271538Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:20.8271630Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:20.8271768Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:12:20.8271833Z         
2025-03-14T05:12:20.8272176Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:20.8272332Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:20.8272455Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:20.8272521Z         
2025-03-14T05:12:20.8272830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:20.8272983Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:20.8273106Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:20.8273172Z         
2025-03-14T05:12:20.8273474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:20.8273626Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:20.8273747Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:12:20.8273827Z         
2025-03-14T05:12:20.8274141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:20.8274323Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:20.8274442Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:12:20.8274508Z         
2025-03-14T05:12:20.8274875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:20.8275016Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:20.8275091Z         
2025-03-14T05:12:20.8275423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:20.8275568Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:20.8275634Z         
2025-03-14T05:12:20.8275988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:20.8276128Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:20.8276260Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:12:20.8276411Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:20.8276560Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:12:20.8276624Z         
2025-03-14T05:12:20.8277003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:20.8277158Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:20.8277288Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:12:20.8277447Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:20.8277584Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:12:20.8277656Z         
2025-03-14T05:12:20.8277984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:20.8278108Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:20.8278270Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:20.8278408Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:12:20.8278474Z         
2025-03-14T05:12:20.8278813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:20.8278931Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:20.8279108Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:20.8279241Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:12:20.8279324Z         
2025-03-14T05:12:20.8279636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:20.8279757Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:20.8279875Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:20.8279949Z         
2025-03-14T05:12:20.8280253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:20.8280354Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:20.8280485Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:20.8280562Z         
2025-03-14T05:12:20.8280864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:20.8280981Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:20.8281106Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:20.8281179Z         
2025-03-14T05:12:20.8281605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:20.8281733Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:20.8281856Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:20.8281932Z         
2025-03-14T05:12:20.8282278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:20.8282463Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:20.8282528Z         
2025-03-14T05:12:20.8282871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:20.8283080Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:20.8283179Z         
2025-03-14T05:12:20.8283569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:20.8283787Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:20.8283853Z         
2025-03-14T05:12:20.8284340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:20.8284476Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:20.8284552Z         
2025-03-14T05:12:20.8284846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8284997Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:12:20.8285065Z         
2025-03-14T05:12:20.8285509Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:20.8285626Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:12:20.8285738Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:20.8285855Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:20.8285929Z         
2025-03-14T05:12:20.8286428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:20.8286601Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:20.8286867Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:12:20.8286933Z         
2025-03-14T05:12:20.8287396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:20.8287564Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:20.8287636Z         
2025-03-14T05:12:20.8287932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8288091Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:20.8288158Z         
2025-03-14T05:12:20.8288547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:20.8288695Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:20.8288765Z         
2025-03-14T05:12:20.8289061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.8289213Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:12:20.8289279Z         
2025-03-14T05:12:20.8289676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:20.8289832Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:20.8289905Z         
2025-03-14T05:12:20.8290384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:20.8290528Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:12:20.8290647Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:20.8290807Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:20.8290938Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:20.8291016Z         
2025-03-14T05:12:20.8291382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:20.8291509Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:20.8291575Z         
2025-03-14T05:12:20.8291585Z 
2025-03-14T05:12:20.8291688Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:20.8333185Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1156, 1199][4158132, 1386044, 1199, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:12:20.8333619Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:12:20.8333969Z         l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8334377Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8334795Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8335192Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8335585Z         l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8335909Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8336330Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8336754Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8337150Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8337526Z         l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8337858Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8338265Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8338696Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8339096Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8339466Z         l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8339790Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8340196Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8340603Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8340994Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8341381Z         l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8341742Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.8342168Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8342597Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8343006Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.8343421Z         l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.8343735Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8344194Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8344602Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8344966Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8345344Z         l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8345657Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8346033Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8346421Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8346788Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8347135Z         l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8347459Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8347833Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8348205Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8348552Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8348909Z         l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8349236Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8349605Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8349999Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8350362Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8350718Z         l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8351035Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8351428Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8351793Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8352154Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8352515Z         l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8352832Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8353232Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8353618Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8353978Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8354330Z         l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8354648Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8355020Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8355396Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8355758Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8356097Z         l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8356413Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8356808Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8357188Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8357548Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8357885Z         l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8358163Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8358501Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8358839Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8359154Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8359466Z         l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8359758Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.8360122Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8360477Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8360811Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.8361122Z         l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.8361407Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8361742Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8362070Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8362384Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8362683Z         l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8362984Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8363313Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8363693Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8364039Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8364365Z         l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8364654Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8364985Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8365367Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8365717Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8366027Z         l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8366319Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8366673Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8366998Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8367316Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8367627Z         l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8367904Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8368240Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8368563Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8368881Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8369206Z         l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8369538Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8369920Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8370291Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8370608Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8370908Z         l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8371191Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8371523Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8371855Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8372167Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8372493Z         l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8372781Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8373120Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8373452Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8373797Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8374139Z         l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8374415Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8374751Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8375077Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8375395Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8375715Z         l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8376030Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8376420Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8376785Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8377156Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8377494Z         l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8377812Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8378194Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8378568Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8378926Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8379300Z         l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8379634Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8380013Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8380393Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8380746Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8381098Z         l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8381536Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:12:20.8381955Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8382359Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8382742Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:12:20.8383154Z         l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:12:20.8383523Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8383948Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8384438Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8384830Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8385189Z         l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8385512Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8385882Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8386253Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8386635Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8386999Z         l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8387321Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8387692Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8388071Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8388426Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8388777Z         l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8389085Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8389470Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8389847Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8390214Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8390556Z         l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8390879Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8391257Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8391623Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8391982Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8392327Z         l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8392647Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8393035Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8393427Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8393806Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8394159Z         l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8394476Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8394845Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8395217Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8395565Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8395911Z         l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8396225Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8396590Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8396976Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8397324Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8397666Z         l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8397943Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8398278Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8398607Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8398925Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8399260Z         l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8399566Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8399949Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8400331Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8400703Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8401039Z         l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8401358Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8401705Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8402041Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8402364Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8402668Z         l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8402965Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8403342Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8403741Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8404098Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8404458Z         l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8404736Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:12:20.8405072Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8405434Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8405781Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:12:20.8406126Z         l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:12:20.8406433Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:12:20.8406810Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8407155Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8407491Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:12:20.8407790Z         l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:12:20.8408071Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:12:20.8408412Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:12:20.8408780Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:12:20.8409135Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:12:20.8409473Z         l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:12:20.8409859Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:12:20.8410221Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:12:20.8410566Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:12:20.8410982Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:12:20.8411383Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:12:20.8411774Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:12:20.8412148Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:12:20.8412231Z         
2025-03-14T05:12:20.8412542Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8413073Z         x: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8413145Z         
2025-03-14T05:12:20.8413456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8415012Z         x_1: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8415106Z         
2025-03-14T05:12:20.8415425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:12:20.8415583Z         x_2: "f32[4, 64, 578, 600][22195200, 346800, 600, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:12:20.8415663Z         
2025-03-14T05:12:20.8416055Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:12:20.8416325Z         x_3: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:12:20.8416397Z         
2025-03-14T05:12:20.8416685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8417142Z         x_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8417245Z         
2025-03-14T05:12:20.8417536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8419155Z         x_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8419233Z         
2025-03-14T05:12:20.8419528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8419681Z         out: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:12:20.8419749Z         
2025-03-14T05:12:20.8420014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8420456Z         x_6: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8420535Z         
2025-03-14T05:12:20.8420868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8422457Z         x_7: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8422536Z         
2025-03-14T05:12:20.8422834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8422986Z         out_1: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:12:20.8423054Z         
2025-03-14T05:12:20.8423322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8423770Z         x_8: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8423844Z         
2025-03-14T05:12:20.8424118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8425821Z         x_9: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8425901Z         
2025-03-14T05:12:20.8426160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8426613Z         x_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8426681Z         
2025-03-14T05:12:20.8426960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8428589Z         x_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8428672Z         
2025-03-14T05:12:20.8428974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8429121Z         x_9 += x_11;  out_2: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:12:20.8429197Z         
2025-03-14T05:12:20.8429482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8429642Z         out_3: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:12:20.8429709Z         
2025-03-14T05:12:20.8429967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8430392Z         x_12: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8430467Z         
2025-03-14T05:12:20.8430731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8432273Z         x_13: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8432354Z         
2025-03-14T05:12:20.8432636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8432791Z         out_4: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:12:20.8432858Z         
2025-03-14T05:12:20.8433116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8433538Z         x_14: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8433612Z         
2025-03-14T05:12:20.8433873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8435400Z         x_15: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8435501Z         
2025-03-14T05:12:20.8435787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8435941Z         out_5: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:12:20.8436008Z         
2025-03-14T05:12:20.8436267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8436702Z         x_16: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8436776Z         
2025-03-14T05:12:20.8437050Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8438573Z         x_17: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8438662Z         
2025-03-14T05:12:20.8438943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8439104Z         x_17 += out_3;  out_6: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:12:20.8439170Z         
2025-03-14T05:12:20.8439461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8439613Z         out_7: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:12:20.8439689Z         
2025-03-14T05:12:20.8439940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8440363Z         x_18: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8440438Z         
2025-03-14T05:12:20.8440704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8442242Z         x_19: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8442319Z         
2025-03-14T05:12:20.8442608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8442746Z         out_8: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:12:20.8442817Z         
2025-03-14T05:12:20.8443060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8443482Z         x_20: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8443550Z         
2025-03-14T05:12:20.8443807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8445320Z         x_21: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8445386Z         
2025-03-14T05:12:20.8445674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8445825Z         out_9: "f32[4, 64, 289, 300][5548800, 86700, 300, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:12:20.8445892Z         
2025-03-14T05:12:20.8446144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8446568Z         x_22: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8446647Z         
2025-03-14T05:12:20.8446907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8448412Z         x_23: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8448499Z         
2025-03-14T05:12:20.8448773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8448935Z         x_23 += out_7;  out_10: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:12:20.8449000Z         
2025-03-14T05:12:20.8449280Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8449432Z         out_11: "f32[4, 256, 289, 300][22195200, 86700, 300, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:12:20.8449505Z         
2025-03-14T05:12:20.8449744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8450163Z         x_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8450246Z         
2025-03-14T05:12:20.8450514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8452004Z         x_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8452077Z         
2025-03-14T05:12:20.8452368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8452515Z         out_12: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:12:20.8452587Z         
2025-03-14T05:12:20.8452838Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8453268Z         x_26: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8453333Z         
2025-03-14T05:12:20.8453598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8455116Z         x_27: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8455221Z         
2025-03-14T05:12:20.8455508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8455652Z         out_13: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:12:20.8455722Z         
2025-03-14T05:12:20.8455966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8456388Z         x_28: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8456454Z         
2025-03-14T05:12:20.8456725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8458283Z         x_29: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8458357Z         
2025-03-14T05:12:20.8458616Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8459054Z         x_30: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8459129Z         
2025-03-14T05:12:20.8459391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8460961Z         x_31: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8461051Z         
2025-03-14T05:12:20.8461330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8461487Z         x_29 += x_31;  out_14: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:12:20.8461553Z         
2025-03-14T05:12:20.8461843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8462002Z         out_15: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:12:20.8462075Z         
2025-03-14T05:12:20.8462327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8462760Z         x_32: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8462827Z         
2025-03-14T05:12:20.8463101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8464805Z         x_33: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8464897Z         
2025-03-14T05:12:20.8465205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8465359Z         out_16: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:12:20.8465439Z         
2025-03-14T05:12:20.8465707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8466148Z         x_34: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8466214Z         
2025-03-14T05:12:20.8466485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8468012Z         x_35: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8468096Z         
2025-03-14T05:12:20.8468392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8468536Z         out_17: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:12:20.8468609Z         
2025-03-14T05:12:20.8468860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8469292Z         x_36: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8469358Z         
2025-03-14T05:12:20.8469626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8471163Z         x_37: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8471248Z         
2025-03-14T05:12:20.8471534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8471689Z         x_37 += out_15;  out_18: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:12:20.8471765Z         
2025-03-14T05:12:20.8472048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8472208Z         out_19: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:12:20.8472275Z         
2025-03-14T05:12:20.8472536Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8472953Z         x_38: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8473025Z         
2025-03-14T05:12:20.8473288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8474851Z         x_39: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8474940Z         
2025-03-14T05:12:20.8475229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8475383Z         out_20: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:12:20.8475450Z         
2025-03-14T05:12:20.8475710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8476138Z         x_40: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8476212Z         
2025-03-14T05:12:20.8476478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8478021Z         x_41: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8478100Z         
2025-03-14T05:12:20.8478385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8478538Z         out_21: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:12:20.8478605Z         
2025-03-14T05:12:20.8478859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8479284Z         x_42: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8479357Z         
2025-03-14T05:12:20.8479624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8481152Z         x_43: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8481242Z         
2025-03-14T05:12:20.8481701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8481876Z         x_43 += out_19;  out_22: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:12:20.8481943Z         
2025-03-14T05:12:20.8482236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8482388Z         out_23: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:12:20.8482461Z         
2025-03-14T05:12:20.8482710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8483139Z         x_44: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8483254Z         
2025-03-14T05:12:20.8483516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8485061Z         x_45: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8485129Z         
2025-03-14T05:12:20.8485421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8485571Z         out_24: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:12:20.8485638Z         
2025-03-14T05:12:20.8485895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8486318Z         x_46: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8486394Z         
2025-03-14T05:12:20.8486658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8488187Z         x_47: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8488280Z         
2025-03-14T05:12:20.8488566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8488714Z         out_25: "f32[4, 128, 145, 150][2784000, 21750, 150, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:12:20.8488781Z         
2025-03-14T05:12:20.8489039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8489469Z         x_48: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8489545Z         
2025-03-14T05:12:20.8489810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8491651Z         x_49: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8491729Z         
2025-03-14T05:12:20.8492012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8492183Z         x_49 += out_23;  out_26: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:12:20.8492250Z         
2025-03-14T05:12:20.8492545Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8492698Z         out_27: "f32[4, 512, 145, 150][11136000, 21750, 150, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:12:20.8492772Z         
2025-03-14T05:12:20.8493021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8493451Z         x_50: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8493518Z         
2025-03-14T05:12:20.8493817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8495375Z         x_51: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8495451Z         
2025-03-14T05:12:20.8495742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8495878Z         out_28: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:12:20.8495953Z         
2025-03-14T05:12:20.8496214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8496670Z         x_52: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8496767Z         
2025-03-14T05:12:20.8497067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8498675Z         x_53: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8498755Z         
2025-03-14T05:12:20.8499072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8499214Z         out_29: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:12:20.8499290Z         
2025-03-14T05:12:20.8499554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8500022Z         x_54: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8500090Z         
2025-03-14T05:12:20.8500377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8501980Z         x_55: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8502074Z         
2025-03-14T05:12:20.8502353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8502809Z         x_56: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:12:20.8502887Z         
2025-03-14T05:12:20.8503167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8504885Z         x_57: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8504994Z         
2025-03-14T05:12:20.8505290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8505444Z         x_55 += x_57;  out_30: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:12:20.8505511Z         
2025-03-14T05:12:20.8505818Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8505972Z         out_31: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:12:20.8506050Z         
2025-03-14T05:12:20.8506309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8506746Z         x_58: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8506816Z         
2025-03-14T05:12:20.8507099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8508624Z         x_59: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8508704Z         
2025-03-14T05:12:20.8508997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8509132Z         out_32: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:12:20.8509206Z         
2025-03-14T05:12:20.8509452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8509884Z         x_60: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8509948Z         
2025-03-14T05:12:20.8510219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8511765Z         x_61: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8511848Z         
2025-03-14T05:12:20.8512136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8512268Z         out_33: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:12:20.8512338Z         
2025-03-14T05:12:20.8512588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8513012Z         x_62: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8513077Z         
2025-03-14T05:12:20.8513346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8514866Z         x_63: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8514948Z         
2025-03-14T05:12:20.8515240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8515385Z         x_63 += out_31;  out_34: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:12:20.8515462Z         
2025-03-14T05:12:20.8515747Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8515899Z         out_35: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:12:20.8515963Z         
2025-03-14T05:12:20.8516222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8516629Z         x_64: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8516704Z         
2025-03-14T05:12:20.8516964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8518500Z         x_65: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8518575Z         
2025-03-14T05:12:20.8518866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8519009Z         out_36: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:12:20.8519074Z         
2025-03-14T05:12:20.8519336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8519758Z         x_66: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8519832Z         
2025-03-14T05:12:20.8520099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8521665Z         x_67: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8521755Z         
2025-03-14T05:12:20.8522037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8522180Z         out_37: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:12:20.8522247Z         
2025-03-14T05:12:20.8522502Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8522918Z         x_68: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8522992Z         
2025-03-14T05:12:20.8523264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8524739Z         x_69: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8524878Z         
2025-03-14T05:12:20.8525149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8525303Z         x_69 += out_35;  out_38: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:12:20.8525368Z         
2025-03-14T05:12:20.8525650Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8525787Z         out_39: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:12:20.8525858Z         
2025-03-14T05:12:20.8526099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8526507Z         x_70: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8526581Z         
2025-03-14T05:12:20.8526853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8528329Z         x_71: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8528394Z         
2025-03-14T05:12:20.8528678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8528808Z         out_40: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:12:20.8528880Z         
2025-03-14T05:12:20.8529123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8529533Z         x_72: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8529605Z         
2025-03-14T05:12:20.8529860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8531356Z         x_73: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8531421Z         
2025-03-14T05:12:20.8531707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8531837Z         out_41: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:12:20.8531910Z         
2025-03-14T05:12:20.8532152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8532569Z         x_74: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8532642Z         
2025-03-14T05:12:20.8532898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8534396Z         x_75: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8534472Z         
2025-03-14T05:12:20.8534753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8534907Z         x_75 += out_39;  out_42: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:12:20.8534972Z         
2025-03-14T05:12:20.8535252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8535391Z         out_43: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:12:20.8535461Z         
2025-03-14T05:12:20.8535701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8536103Z         x_76: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8536183Z         
2025-03-14T05:12:20.8536445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8537897Z         x_77: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8537969Z         
2025-03-14T05:12:20.8538254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8538385Z         out_44: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:12:20.8538456Z         
2025-03-14T05:12:20.8538703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8539114Z         x_78: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8539178Z         
2025-03-14T05:12:20.8539462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8540925Z         x_79: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8540999Z         
2025-03-14T05:12:20.8541285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8541414Z         out_45: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:12:20.8541482Z         
2025-03-14T05:12:20.8541722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8542134Z         x_80: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8542198Z         
2025-03-14T05:12:20.8542459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8543958Z         x_81: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8544034Z         
2025-03-14T05:12:20.8544377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8544532Z         x_81 += out_43;  out_46: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:12:20.8544608Z         
2025-03-14T05:12:20.8544889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8545041Z         out_47: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:12:20.8545108Z         
2025-03-14T05:12:20.8545361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8545776Z         x_82: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:12:20.8545871Z         
2025-03-14T05:12:20.8546147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8547648Z         x_83: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8547725Z         
2025-03-14T05:12:20.8548007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8548150Z         out_48: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:12:20.8548217Z         
2025-03-14T05:12:20.8548473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8548889Z         x_84: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:12:20.8548979Z         
2025-03-14T05:12:20.8549245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8550778Z         x_85: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8550853Z         
2025-03-14T05:12:20.8551140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8551280Z         out_49: "f32[4, 256, 73, 75][1401600, 5475, 75, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:12:20.8551345Z         
2025-03-14T05:12:20.8551614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8552046Z         x_86: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:12:20.8552120Z         
2025-03-14T05:12:20.8552399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:12:20.8553924Z         x_87: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:12:20.8553999Z         
2025-03-14T05:12:20.8554276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:12:20.8554429Z         x_87 += out_47;  out_50: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:12:20.8554493Z         
2025-03-14T05:12:20.8554793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:12:20.8554933Z         out_51: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:12:20.8555007Z         
2025-03-14T05:12:20.8555446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.8555623Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.8555687Z         
2025-03-14T05:12:20.8555991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8556133Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:20.8556207Z         
2025-03-14T05:12:20.8556654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:20.8556815Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:12:20.8556880Z         
2025-03-14T05:12:20.8557181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8557323Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:12:20.8557400Z         
2025-03-14T05:12:20.8557779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:12:20.8557968Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:12:20.8558069Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:12:20.8558197Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:12:20.8558262Z         
2025-03-14T05:12:20.8558601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:12:20.8558731Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:12:20.8558805Z         
2025-03-14T05:12:20.8559159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:12:20.8559300Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:12:20.8559372Z         
2025-03-14T05:12:20.8559757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:12:20.8559975Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:12:20.8560040Z         
2025-03-14T05:12:20.8560460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:12:20.8560586Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:12:20.8561020Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:12:20.8561142Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:12:20.8561259Z         x_88: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:12:20.8561322Z         
2025-03-14T05:12:20.8561621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.8561745Z         tensor: "f32[82125, 4][4, 1]cpu" = x_88.to(torch.float32);  x_88 = None
2025-03-14T05:12:20.8561834Z         
2025-03-14T05:12:20.8562082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:20.8562846Z         x_89: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(out_51, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  out_51 = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:12:20.8562911Z         
2025-03-14T05:12:20.8563185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:12:20.8563363Z         x_90: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_89, inplace = False);  x_89 = None
2025-03-14T05:12:20.8563437Z         
2025-03-14T05:12:20.8563806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:12:20.8564635Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:12:20.8564707Z         
2025-03-14T05:12:20.8565052Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:12:20.8565855Z         x_91: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_90, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_90 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:12:20.8565935Z         
2025-03-14T05:12:20.8566267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:12:20.8566417Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:12:20.8566564Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:12:20.8566627Z         
2025-03-14T05:12:20.8567038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:12:20.8567201Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_91.view(4, -1, 4, 73, 75);  x_91 = None
2025-03-14T05:12:20.8567371Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:12:20.8567550Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:12:20.8567617Z         
2025-03-14T05:12:20.8568011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:20.8568226Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:12:20.8568299Z         
2025-03-14T05:12:20.8568717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:20.8568882Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:12:20.8569028Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:20.8569172Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:20.8569235Z         
2025-03-14T05:12:20.8569605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:20.8569775Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:12:20.8569849Z         
2025-03-14T05:12:20.8570160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:20.8570305Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:20.8570369Z         
2025-03-14T05:12:20.8570689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:20.8570817Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:20.8570946Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.8571091Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:20.8571168Z         
2025-03-14T05:12:20.8571497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:20.8571639Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:20.8571757Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:20.8571906Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:20.8571970Z         
2025-03-14T05:12:20.8572272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:20.8572390Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:20.8572488Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:20.8572610Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:12:20.8572685Z         
2025-03-14T05:12:20.8572985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:20.8573135Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:20.8573224Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:20.8573356Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:12:20.8573420Z         
2025-03-14T05:12:20.8573741Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:20.8573898Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:20.8574035Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:20.8574109Z         
2025-03-14T05:12:20.8574406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:20.8574560Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:20.8574686Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:20.8574760Z         
2025-03-14T05:12:20.8575051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:20.8575207Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:20.8575318Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:12:20.8575390Z         
2025-03-14T05:12:20.8575681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:20.8575874Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:20.8575984Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:12:20.8576058Z         
2025-03-14T05:12:20.8576383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:20.8576526Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:20.8576590Z         
2025-03-14T05:12:20.8576916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:20.8577069Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:20.8577155Z         
2025-03-14T05:12:20.8577501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:20.8577646Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:20.8577768Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:12:20.8577922Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:20.8578055Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:12:20.8578127Z         
2025-03-14T05:12:20.8578462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:20.8578603Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:20.8578722Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:12:20.8578875Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:20.8579009Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:12:20.8579081Z         
2025-03-14T05:12:20.8579399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:20.8579519Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:20.8579698Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:20.8579826Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:12:20.8579897Z         
2025-03-14T05:12:20.8580213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:20.8580348Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:20.8580510Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:20.8580647Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:12:20.8580714Z         
2025-03-14T05:12:20.8581019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:20.8581115Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:20.8581238Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:20.8581304Z         
2025-03-14T05:12:20.8581753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:20.8581855Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:20.8581978Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:20.8582044Z         
2025-03-14T05:12:20.8582350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:20.8582463Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:20.8582600Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:20.8582664Z         
2025-03-14T05:12:20.8582978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:20.8583104Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:20.8583235Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:20.8583300Z         
2025-03-14T05:12:20.8583647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:20.8583824Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:20.8583898Z         
2025-03-14T05:12:20.8584282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:20.8584458Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:20.8584523Z         
2025-03-14T05:12:20.8584912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:20.8585092Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:20.8585169Z         
2025-03-14T05:12:20.8585644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:20.8585799Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:20.8585864Z         
2025-03-14T05:12:20.8586162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8586302Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:12:20.8586376Z         
2025-03-14T05:12:20.8586808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:20.8586929Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:12:20.8587031Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:20.8587149Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:20.8587213Z         
2025-03-14T05:12:20.8587666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:20.8587827Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:20.8588064Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:12:20.8588135Z         
2025-03-14T05:12:20.8588577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:20.8588746Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:20.8588812Z         
2025-03-14T05:12:20.8589121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:20.8589286Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:20.8589357Z         
2025-03-14T05:12:20.8589729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:20.8589877Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:20.8589939Z         
2025-03-14T05:12:20.8590233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:20.8590372Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:12:20.8590444Z         
2025-03-14T05:12:20.8590807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:20.8590950Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:20.8591014Z         
2025-03-14T05:12:20.8591487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:20.8591627Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:12:20.8591750Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:20.8591901Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:20.8592060Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:20.8592126Z         
2025-03-14T05:12:20.8592494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:20.8592611Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:20.8592700Z         
2025-03-14T05:12:25.4465397Z 
2025-03-14T05:12:25.4545341Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:25.4589740Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:12:25.4625131Z         l_features_res4_ = L_features_res4_
2025-03-14T05:12:25.4666457Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:12:25.4704998Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:12:25.4747546Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:12:25.4785205Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:12:25.4786544Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:12:25.4800503Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:12:25.4801230Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:12:25.4801684Z         
2025-03-14T05:12:25.4802349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:25.4803145Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:12:25.4803458Z         
2025-03-14T05:12:25.4803908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4804484Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:25.4804778Z         
2025-03-14T05:12:25.4805371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:25.4806084Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:12:25.4806385Z         
2025-03-14T05:12:25.4806814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4807366Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:12:25.4807727Z         
2025-03-14T05:12:25.4808247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:12:25.4808929Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:12:25.4809304Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:12:25.4809675Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:12:25.4809947Z         
2025-03-14T05:12:25.4810419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:12:25.4810969Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:12:25.4811226Z         
2025-03-14T05:12:25.4811659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:12:25.4812182Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:12:25.4812431Z         
2025-03-14T05:12:25.4812915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:12:25.4813592Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:12:25.4813926Z         
2025-03-14T05:12:25.4814456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:12:25.4815087Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:12:25.4815633Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:12:25.4816156Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:12:25.4816462Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:12:25.4816694Z         
2025-03-14T05:12:25.4817104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:25.4817596Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:12:25.4817845Z         
2025-03-14T05:12:25.4818275Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:25.4819232Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:12:25.4819975Z         
2025-03-14T05:12:25.4820356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:12:25.4820899Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:12:25.4821212Z         
2025-03-14T05:12:25.4821700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:12:25.4822834Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:12:25.4823578Z         
2025-03-14T05:12:25.4824048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:12:25.4825208Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:12:25.4825951Z         
2025-03-14T05:12:25.4826410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:12:25.4826986Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:12:25.4827361Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:12:25.4827623Z         
2025-03-14T05:12:25.4828126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:12:25.4828750Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:12:25.4829132Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:12:25.4829550Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:12:25.4829864Z         
2025-03-14T05:12:25.4830356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:25.4831013Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:12:25.4831333Z         
2025-03-14T05:12:25.4831851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:25.4832482Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:12:25.4832836Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:25.4833177Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:25.4833435Z         
2025-03-14T05:12:25.4833896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:25.4834492Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:12:25.4834781Z         
2025-03-14T05:12:25.4835179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:25.4835689Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:25.4835970Z         
2025-03-14T05:12:25.4836373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:25.4836875Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:25.4837184Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:25.4837525Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:25.4837795Z         
2025-03-14T05:12:25.4838200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:25.4838698Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:25.4839000Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:25.4839323Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:25.4839597Z         
2025-03-14T05:12:25.4839994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:25.4840485Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:25.4840758Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:25.4841024Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:12:25.4841294Z         
2025-03-14T05:12:25.4841700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:25.4842209Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:25.4842503Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:25.4842796Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:12:25.4843063Z         
2025-03-14T05:12:25.4843486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:25.4843996Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:25.4844326Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:25.4844563Z         
2025-03-14T05:12:25.4844961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:25.4845464Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:25.4845792Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:25.4846029Z         
2025-03-14T05:12:25.4846425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:25.4846935Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:25.4847262Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:12:25.4847498Z         
2025-03-14T05:12:25.4847892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:25.4848431Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:25.4848777Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:12:25.4849037Z         
2025-03-14T05:12:25.4849466Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:25.4850004Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:25.4850265Z         
2025-03-14T05:12:25.4850709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:25.4851233Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:25.4851494Z         
2025-03-14T05:12:25.4851931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:25.4852479Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:25.4852811Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:12:25.4853161Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:25.4853517Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:12:25.4853787Z         
2025-03-14T05:12:25.4854234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:25.4854779Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:25.4855103Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:12:25.4855434Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:25.4855824Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:12:25.4856101Z         
2025-03-14T05:12:25.4856526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:25.4857025Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:25.4857353Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:25.4857702Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:12:25.4857961Z         
2025-03-14T05:12:25.4858388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:25.4858893Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:25.4859234Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:25.4859589Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:12:25.4859842Z         
2025-03-14T05:12:25.4860253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:25.4860726Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:25.4860999Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:25.4861241Z         
2025-03-14T05:12:25.4861637Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:25.4862135Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:25.4862402Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:25.4862646Z         
2025-03-14T05:12:25.4863041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:25.4863522Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:25.4863842Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:25.4864094Z         
2025-03-14T05:12:25.4864653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:25.4865170Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:25.4865479Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:25.4865751Z         
2025-03-14T05:12:25.4866208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:25.4866825Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:25.4867139Z         
2025-03-14T05:12:25.4867591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:25.4868175Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:25.4868479Z         
2025-03-14T05:12:25.4868979Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:25.4869660Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:25.4869989Z         
2025-03-14T05:12:25.4870594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:25.4871313Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:25.4871581Z         
2025-03-14T05:12:25.4871985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4872511Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:12:25.4872788Z         
2025-03-14T05:12:25.4873335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:25.4873970Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:12:25.4874261Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:25.4874531Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:25.4874767Z         
2025-03-14T05:12:25.4875320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:25.4875994Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:25.4876453Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:12:25.4876832Z         
2025-03-14T05:12:25.4877385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:25.4878071Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:25.4878390Z         
2025-03-14T05:12:25.4878780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4879286Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:25.4879566Z         
2025-03-14T05:12:25.4880040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:25.4880626Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:25.4880894Z         
2025-03-14T05:12:25.4881268Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:25.4882096Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:12:25.4882359Z         
2025-03-14T05:12:25.4882828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:25.4883401Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:25.4883665Z         
2025-03-14T05:12:25.4884314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:25.4885022Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:12:25.4885341Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:25.4885673Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:25.4886018Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:25.4886278Z         
2025-03-14T05:12:25.4886744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:25.4887289Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:25.4887531Z         
2025-03-14T05:12:25.4887633Z 
2025-03-14T05:12:25.4887725Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:25.4889053Z     def forward(self, L_features_res4_: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[15, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[1024, 1024, 3, 3][9216, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[1024][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[15, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[15][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[60, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[60][1]cpu"):
2025-03-14T05:12:25.4890361Z         l_features_res4_ = L_features_res4_
2025-03-14T05:12:25.4890787Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:12:25.4891318Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:12:25.4892639Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:12:25.4893666Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:12:25.4894357Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:12:25.4895051Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:12:25.4895731Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:12:25.4896177Z         
2025-03-14T05:12:25.4896842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:25.4897618Z         arange: "f32[75][1]cpu" = torch.arange(0.0, 1200, step = 16, dtype = torch.float32)
2025-03-14T05:12:25.4897909Z         
2025-03-14T05:12:25.4898307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4898806Z         shifts_x: "f32[75][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:25.4899069Z         
2025-03-14T05:12:25.4899601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:12:25.4900271Z         arange_1: "f32[73][1]cpu" = torch.arange(0.0, 1168, step = 16, dtype = torch.float32)
2025-03-14T05:12:25.4900570Z         
2025-03-14T05:12:25.4900955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4901454Z         shifts_y: "f32[73][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:12:25.4901715Z         
2025-03-14T05:12:25.4902187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:12:25.4902798Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:12:25.4903135Z         shift_y: "f32[73, 75][1, 0]cpu" = meshgrid[0]
2025-03-14T05:12:25.4903410Z         shift_x: "f32[73, 75][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:12:25.4903648Z         
2025-03-14T05:12:25.4904068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:12:25.4904691Z         shift_x_1: "f32[5475][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:12:25.4904934Z         
2025-03-14T05:12:25.4905356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:12:25.4905868Z         shift_y_1: "f32[5475][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:12:25.4906111Z         
2025-03-14T05:12:25.4906587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:12:25.4907281Z         shifts: "f32[5475, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:12:25.4907615Z         
2025-03-14T05:12:25.4908125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:12:25.4908753Z         view: "f32[5475, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:12:25.4909255Z         view_1: "f32[1, 15, 4][60, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:12:25.4909743Z         add: "f32[5475, 15, 4][60, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:12:25.4910031Z         x: "f32[82125, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:12:25.4910267Z         
2025-03-14T05:12:25.4910659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:25.4911142Z         tensor: "f32[82125, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:12:25.4911385Z         
2025-03-14T05:12:25.4911739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:12:25.4912661Z         x_1: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.conv2d(l_features_res4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_res4_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:12:25.4913374Z         
2025-03-14T05:12:25.4913743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:12:25.4914274Z         x_2: "f32[4, 1024, 73, 75][5606400, 5475, 75, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:12:25.4914604Z         
2025-03-14T05:12:25.4915063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:12:25.4916092Z         score: "f32[4, 15, 73, 75][82125, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:12:25.4916810Z         
2025-03-14T05:12:25.4917261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:12:25.4918276Z         x_3: "f32[4, 60, 73, 75][328500, 5475, 75, 1]cpu" = torch.conv2d(x_2, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_2 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:12:25.4919023Z         
2025-03-14T05:12:25.4919450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:12:25.4919995Z         permute: "f32[4, 73, 75, 15][82125, 75, 1, 5475]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:12:25.4920359Z         logits_i: "f32[4, 82125][82125, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:12:25.4920626Z         
2025-03-14T05:12:25.4921134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:12:25.4921753Z         view_2: "f32[4, 15, 4, 73, 75][328500, 21900, 5475, 75, 1]cpu" = x_3.view(4, -1, 4, 73, 75);  x_3 = None
2025-03-14T05:12:25.4922149Z         permute_1: "f32[4, 73, 75, 15, 4][328500, 75, 1, 21900, 5475]cpu" = view_2.permute(0, 3, 4, 1, 2);  view_2 = None
2025-03-14T05:12:25.4922558Z         pred_anchor_deltas_i: "f32[4, 82125, 4][328500, 4, 1]cpu" = permute_1.flatten(1, -2);  permute_1 = None
2025-03-14T05:12:25.4922852Z         
2025-03-14T05:12:25.4923344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:25.4924005Z         pred_anchor_deltas_i_1: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:12:25.4924333Z         
2025-03-14T05:12:25.4924852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:25.4926369Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:12:25.4926730Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:25.4927060Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:25.4927321Z         
2025-03-14T05:12:25.4927783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:25.4928415Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:12:25.4928721Z         
2025-03-14T05:12:25.4929115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:25.4929616Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:25.4929872Z         
2025-03-14T05:12:25.4930267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:25.4930763Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:25.4931066Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:25.4931393Z         widths: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:25.4931661Z         
2025-03-14T05:12:25.4932080Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:25.4932561Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:25.4932857Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:25.4933174Z         heights: "f32[328500][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:25.4933439Z         
2025-03-14T05:12:25.4933831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:25.4934314Z         getitem_6: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:25.4934600Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:25.4934865Z         ctr_x: "f32[328500][1]cpu" = getitem_6 + mul;  getitem_6 = mul = None
2025-03-14T05:12:25.4935106Z         
2025-03-14T05:12:25.4935499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:25.4936019Z         getitem_7: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:25.4936306Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:25.4936572Z         ctr_y: "f32[328500][1]cpu" = getitem_7 + mul_1;  getitem_7 = mul_1 = None
2025-03-14T05:12:25.4936816Z         
2025-03-14T05:12:25.4937232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:25.4937732Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:25.4938059Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:25.4938294Z         
2025-03-14T05:12:25.4938682Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:25.4939174Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:25.4939495Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:25.4939728Z         
2025-03-14T05:12:25.4940103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:25.4940593Z         getitem_10: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:25.4940913Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_10 / 1.0;  getitem_10 = None
2025-03-14T05:12:25.4941152Z         
2025-03-14T05:12:25.4941557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:25.4942101Z         getitem_11: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:25.4942447Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_11 / 1.0;  getitem_11 = None
2025-03-14T05:12:25.4942677Z         
2025-03-14T05:12:25.4943085Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:25.4943620Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:25.4943873Z         
2025-03-14T05:12:25.4944396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:25.4944944Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:25.4945206Z         
2025-03-14T05:12:25.4945651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:25.4947162Z         getitem_12: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:25.4947507Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_12;  dx = getitem_12 = None
2025-03-14T05:12:25.4947836Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:25.4948182Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_13;  mul_2 = getitem_13 = None
2025-03-14T05:12:25.4948484Z         
2025-03-14T05:12:25.4948925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:25.4949471Z         getitem_14: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:25.4949793Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_14;  dy = getitem_14 = None
2025-03-14T05:12:25.4950138Z         getitem_15: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:25.4950484Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_15;  mul_3 = getitem_15 = None
2025-03-14T05:12:25.4950740Z         
2025-03-14T05:12:25.4951161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:25.4951664Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:25.4951988Z         getitem_16: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:25.4952340Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_16;  exp = getitem_16 = None
2025-03-14T05:12:25.4952591Z         
2025-03-14T05:12:25.4953012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:25.4953508Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:25.4953843Z         getitem_17: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:25.4954192Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_17;  exp_1 = getitem_17 = None
2025-03-14T05:12:25.4954445Z         
2025-03-14T05:12:25.4954851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:25.4955331Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:25.4955614Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:25.4955857Z         
2025-03-14T05:12:25.4956252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:25.4956712Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:25.4956973Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:25.4957210Z         
2025-03-14T05:12:25.4957605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:25.4958081Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:25.4958378Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:25.4958634Z         
2025-03-14T05:12:25.4959027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:25.4959502Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:25.4959793Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:25.4960033Z         
2025-03-14T05:12:25.4960463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:25.4961039Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:25.4961334Z         
2025-03-14T05:12:25.4961786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:25.4962332Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:25.4962612Z         
2025-03-14T05:12:25.4963093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:25.4963685Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:25.4963969Z         
2025-03-14T05:12:25.4964526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:25.4965194Z         arange_2: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:25.4965441Z         
2025-03-14T05:12:25.4965814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4966294Z         batch_idx: "i64[4][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:12:25.4966551Z         
2025-03-14T05:12:25.4967068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:25.4967665Z         topk = logits_i.topk(6000, dim = 1);  logits_i = None
2025-03-14T05:12:25.4967936Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:25.4968202Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:25.4968436Z         
2025-03-14T05:12:25.4968997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:25.4969670Z         getitem_20: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:25.4970115Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_20, topk_idx)];  proposals_i_1 = getitem_20 = topk_idx = None
2025-03-14T05:12:25.4970454Z         
2025-03-14T05:12:25.4970999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:25.4971675Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:25.4971961Z         
2025-03-14T05:12:25.4972340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:25.4972846Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:25.4973117Z         
2025-03-14T05:12:25.4973586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:25.4974169Z         getitem_22: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:25.4974437Z         
2025-03-14T05:12:25.4974824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:25.4975321Z         tensor_1: "f32[6000, 4][4, 1]cpu" = getitem_22.to(torch.float32);  getitem_22 = None
2025-03-14T05:12:25.4975607Z         
2025-03-14T05:12:25.4976076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:25.4976648Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:25.4976912Z         
2025-03-14T05:12:25.4977499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:25.4978168Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor_1);  tensor_1 = None
2025-03-14T05:12:25.4978482Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:25.4978810Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:25.4979151Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:25.4979407Z         
2025-03-14T05:12:25.4979863Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:25.4980401Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:25.4980639Z         
2025-03-14T05:12:26.0199105Z 
2025-03-14T05:12:26.0203479Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:26.0207726Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 82125, 4][328500, 4, 1]cpu", L_anchors_0_tensor: "f32[82125, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 82125][82125, 1]cpu"):
2025-03-14T05:12:26.0208695Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:12:26.0209029Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:12:26.0209372Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:12:26.0209962Z         
2025-03-14T05:12:26.0210681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:12:26.0211433Z         pred_anchor_deltas_i: "f32[328500, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:12:26.0211782Z         
2025-03-14T05:12:26.0212323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:12:26.0213009Z         unsqueeze: "f32[1, 82125, 4][328500, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:12:26.0213407Z         expand: "f32[4, 82125, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:12:26.0213756Z         anchors_i: "f32[328500, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:12:26.0214020Z         
2025-03-14T05:12:26.0214495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:26.0215093Z         deltas: "f32[328500, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:12:26.0215382Z         
2025-03-14T05:12:26.0215786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:26.0216299Z         boxes: "f32[328500, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:12:26.0216564Z         
2025-03-14T05:12:26.0217024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:26.0217533Z         getitem: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:26.0217842Z         getitem_1: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:26.0218169Z         widths: "f32[328500][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:12:26.0218435Z         
2025-03-14T05:12:26.0218903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:26.0219405Z         getitem_2: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:26.0219709Z         getitem_3: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:26.0220033Z         heights: "f32[328500][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:12:26.0220304Z         
2025-03-14T05:12:26.0220705Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:26.0221197Z         getitem_4: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:26.0221466Z         mul: "f32[328500][1]cpu" = 0.5 * widths
2025-03-14T05:12:26.0221732Z         ctr_x: "f32[328500][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:12:26.0221976Z         
2025-03-14T05:12:26.0222373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:26.0222881Z         getitem_5: "f32[328500][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:26.0223172Z         mul_1: "f32[328500][1]cpu" = 0.5 * heights
2025-03-14T05:12:26.0223449Z         ctr_y: "f32[328500][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:12:26.0223697Z         
2025-03-14T05:12:26.0224477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:26.0225045Z         getitem_6: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:26.0225398Z         dx: "f32[328500, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:12:26.0225637Z         
2025-03-14T05:12:26.0226033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:26.0226536Z         getitem_7: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:26.0226861Z         dy: "f32[328500, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:12:26.0227090Z         
2025-03-14T05:12:26.0227476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:26.0227972Z         getitem_8: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:26.0228287Z         dw: "f32[328500, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:12:26.0228520Z         
2025-03-14T05:12:26.0228921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:26.0229460Z         getitem_9: "f32[328500, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:26.0229805Z         dh: "f32[328500, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:12:26.0230038Z         
2025-03-14T05:12:26.0230467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:26.0231033Z         dw_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:26.0231296Z         
2025-03-14T05:12:26.0231720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:26.0232276Z         dh_1: "f32[328500, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:26.0232533Z         
2025-03-14T05:12:26.0232957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:26.0233492Z         getitem_10: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:26.0233811Z         mul_2: "f32[328500, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:12:26.0234146Z         getitem_11: "f32[328500, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:26.0234496Z         pred_ctr_x: "f32[328500, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:12:26.0234756Z         
2025-03-14T05:12:26.0235187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:26.0235726Z         getitem_12: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:26.0236039Z         mul_3: "f32[328500, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:12:26.0236365Z         getitem_13: "f32[328500, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:26.0236708Z         pred_ctr_y: "f32[328500, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:12:26.0236968Z         
2025-03-14T05:12:26.0237402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:26.0237935Z         exp: "f32[328500, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:26.0238265Z         getitem_14: "f32[328500, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:26.0238609Z         pred_w: "f32[328500, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:12:26.0238864Z         
2025-03-14T05:12:26.0239286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:26.0239792Z         exp_1: "f32[328500, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:26.0240130Z         getitem_15: "f32[328500, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:26.0241315Z         pred_h: "f32[328500, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:12:26.0241590Z         
2025-03-14T05:12:26.0242009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:26.0242711Z         mul_6: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:26.0243005Z         x1: "f32[328500, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:26.0243250Z         
2025-03-14T05:12:26.0243663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:26.0244131Z         mul_7: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:26.0244399Z         y1: "f32[328500, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:26.0244668Z         
2025-03-14T05:12:26.0245057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:26.0245533Z         mul_8: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:26.0245821Z         x2: "f32[328500, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:26.0246073Z         
2025-03-14T05:12:26.0246484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:26.0246960Z         mul_9: "f32[328500, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:26.0247255Z         y2: "f32[328500, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:26.0247502Z         
2025-03-14T05:12:26.0247936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:26.0248518Z         pred_boxes: "f32[328500, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:26.0248825Z         
2025-03-14T05:12:26.0249236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:26.0249770Z         proposals_i: "f32[328500, 4][4, 1]cpu" = pred_boxes.reshape((328500, 4));  pred_boxes = None
2025-03-14T05:12:26.0250045Z         
2025-03-14T05:12:26.0250505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:12:26.0251090Z         proposals_i_1: "f32[4, 82125, 4][328500, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:12:26.0251376Z         
2025-03-14T05:12:26.0251942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:12:26.0252637Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:12:26.0252880Z         
2025-03-14T05:12:26.0253262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:26.0253751Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:12:26.0254009Z         
2025-03-14T05:12:26.0254543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:12:26.0255187Z         topk = l_pred_objectness_logits_0_.topk(6000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:12:26.0255520Z         topk_scores_i: "f32[4, 6000][6000, 1]cpu" = topk[0]
2025-03-14T05:12:26.0255791Z         topk_idx: "i64[4, 6000][6000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:12:26.0256027Z         
2025-03-14T05:12:26.0256574Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:12:26.0257254Z         getitem_18: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:12:26.0257708Z         topk_proposals_i: "f32[4, 6000, 4][24000, 4, 1]cpu" = proposals_i_1[(getitem_18, topk_idx)];  proposals_i_1 = getitem_18 = topk_idx = None
2025-03-14T05:12:26.0258120Z         
2025-03-14T05:12:26.0258701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:12:26.0259381Z         full: "i64[6000][1]cpu" = torch.full((6000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:12:26.0259670Z         
2025-03-14T05:12:26.0260068Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:12:26.0260584Z         level_ids: "i64[6000][1]cpu" = full.to(device(type='cpu'));  full = level_ids = None
2025-03-14T05:12:26.0260864Z         
2025-03-14T05:12:26.0261341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:12:26.0261927Z         getitem_20: "f32[6000, 4][4, 1]cpu" = topk_proposals_i[0];  topk_proposals_i = None
2025-03-14T05:12:26.0262196Z         
2025-03-14T05:12:26.0262582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:12:26.0263076Z         tensor: "f32[6000, 4][4, 1]cpu" = getitem_20.to(torch.float32);  getitem_20 = None
2025-03-14T05:12:26.0263339Z         
2025-03-14T05:12:26.0263803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:12:26.0264438Z         scores_per_img: "f32[6000][1]cpu" = topk_scores_i[0];  topk_scores_i = None
2025-03-14T05:12:26.0264710Z         
2025-03-14T05:12:26.0265310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:12:26.0266042Z         isfinite: "b8[6000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:12:26.0266374Z         all_1: "b8[6000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:26.0266704Z         isfinite_1: "b8[6000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:12:26.0267056Z         valid_mask: "b8[6000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:12:26.0267324Z         
2025-03-14T05:12:26.0267798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:12:26.0268354Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:12:26.0268603Z         
2025-03-14T05:12:41.2888082Z 
2025-03-14T05:12:41.2893007Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:41.2897264Z     def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:41.2898707Z         l_stack0_ = L_stack0_
2025-03-14T05:12:41.2899110Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:12:41.2900959Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:12:41.2901533Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:12:41.2902096Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:12:41.2902660Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2903081Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2903489Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2903894Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2904350Z         
2025-03-14T05:12:41.2905109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:12:41.2905836Z         mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:12:41.2906115Z         
2025-03-14T05:12:41.2906529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:12:41.2907525Z         scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:12:41.2908241Z         
2025-03-14T05:12:41.2908711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:12:41.2910248Z         proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:12:41.2911013Z         
2025-03-14T05:12:41.2911396Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.2911862Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.2912121Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:41.2912361Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:41.2912638Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.2912908Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:41.2913162Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:41.2913384Z         
2025-03-14T05:12:41.2913759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:41.2914691Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.2915406Z         
2025-03-14T05:12:41.2915865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:41.2916484Z         deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:12:41.2916761Z         
2025-03-14T05:12:41.2917160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:41.2917700Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:41.2917982Z         
2025-03-14T05:12:41.2918388Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:41.2918894Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:41.2919203Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.2919520Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:41.2919787Z         
2025-03-14T05:12:41.2920198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:41.2920695Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:41.2920993Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:41.2921312Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:41.2921577Z         
2025-03-14T05:12:41.2921970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:41.2922456Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.2922720Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:41.2923004Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:41.2923262Z         
2025-03-14T05:12:41.2923663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:41.2924174Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:41.2924462Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:41.2924729Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:41.2924972Z         
2025-03-14T05:12:41.2925393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:41.2925903Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:41.2926225Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:41.2926458Z         
2025-03-14T05:12:41.2926849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:41.2927356Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:41.2927679Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:41.2927912Z         
2025-03-14T05:12:41.2928298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:41.2928797Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:41.2929136Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:41.2929370Z         
2025-03-14T05:12:41.2929761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:41.2930291Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:41.2930653Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:41.2930888Z         
2025-03-14T05:12:41.2931315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:41.2931842Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:41.2932119Z         
2025-03-14T05:12:41.2932775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:41.2933317Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:41.2933579Z         
2025-03-14T05:12:41.2934017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:41.2934554Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:41.2934873Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:41.2935201Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:41.2935544Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:41.2935805Z         
2025-03-14T05:12:41.2936306Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:41.2936870Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:41.2937188Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:41.2937523Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:41.2937870Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:41.2938134Z         
2025-03-14T05:12:41.2938569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:41.2939090Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:41.2939434Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:41.2939789Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:41.2940050Z         
2025-03-14T05:12:41.2940834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:41.2941352Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:41.2941689Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:41.2942039Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:41.2942298Z         
2025-03-14T05:12:41.2942702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:41.2943209Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:41.2943483Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:41.2943727Z         
2025-03-14T05:12:41.2944142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:41.2944724Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:41.2945000Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:41.2945249Z         
2025-03-14T05:12:41.2945643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:41.2946115Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:41.2946403Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:41.2946653Z         
2025-03-14T05:12:41.2947147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:41.2947731Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:41.2948022Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:41.2948271Z         
2025-03-14T05:12:41.2948702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:41.2949278Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:41.2949573Z         
2025-03-14T05:12:41.2950011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:41.2950580Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:41.2950869Z         
2025-03-14T05:12:41.2951313Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:41.2951923Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:41.2952288Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:41.2952581Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:41.2952888Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:41.2953209Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:41.2953474Z         
2025-03-14T05:12:41.2953861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.2954428Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.2954782Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:41.2955027Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:41.2955395Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.2955747Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:41.2955989Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:41.2956215Z         
2025-03-14T05:12:41.2956659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:41.2957215Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:12:41.2957506Z         
2025-03-14T05:12:41.2957972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:41.2958573Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:41.2958936Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:41.2959227Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:41.2959531Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:41.2959850Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:41.2960111Z         
2025-03-14T05:12:41.2960664Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:41.2961357Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:41.2961699Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:41.2962041Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:41.2962382Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:41.2962677Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:41.2962920Z         
2025-03-14T05:12:41.2963375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:41.2963913Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:41.2964146Z         
2025-03-14T05:12:41.2964295Z 
2025-03-14T05:12:41.2964397Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:41.2965765Z     def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:41.2967088Z         l_stack0_ = L_stack0_
2025-03-14T05:12:41.2967477Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:12:41.2968038Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:12:41.2968596Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:12:41.2969148Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:12:41.2969617Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2970046Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2970450Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2970851Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:41.2971151Z         
2025-03-14T05:12:41.2971710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:12:41.2972342Z         mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:12:41.2972611Z         
2025-03-14T05:12:41.2973009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:12:41.2973975Z         scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:12:41.2974691Z         
2025-03-14T05:12:41.2975103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:12:41.2976108Z         proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:12:41.2976852Z         
2025-03-14T05:12:41.2977240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.2977720Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.2978011Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:41.2978250Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:41.2978530Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.2978797Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:41.2979050Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:41.2979281Z         
2025-03-14T05:12:41.2979669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:41.2980630Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.2981362Z         
2025-03-14T05:12:41.2982835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:41.2983475Z         deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:12:41.2983773Z         
2025-03-14T05:12:41.2984282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:41.2984859Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:41.2985275Z         
2025-03-14T05:12:41.2985727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:41.2986295Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:41.2986702Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.2987234Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:41.2987511Z         
2025-03-14T05:12:41.2987932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:41.2988445Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:41.2988753Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:41.2989075Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:41.2989345Z         
2025-03-14T05:12:41.2989756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:41.2990256Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.2990521Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:41.2990787Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:41.2991034Z         
2025-03-14T05:12:41.2991446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:41.2991966Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:41.2992258Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:41.2992532Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:41.2992812Z         
2025-03-14T05:12:41.2993267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:41.2993857Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:41.2994282Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:41.2994527Z         
2025-03-14T05:12:41.2994929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:41.2995522Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:41.2995892Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:41.2996132Z         
2025-03-14T05:12:41.2996526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:41.2997041Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:41.2997366Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:41.2997612Z         
2025-03-14T05:12:41.2998000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:41.2998531Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:41.2998871Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:41.2999129Z         
2025-03-14T05:12:41.2999557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:41.3000084Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:41.3000346Z         
2025-03-14T05:12:41.3000786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:41.3001311Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:41.3001569Z         
2025-03-14T05:12:41.3002003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:41.3002537Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:41.3002855Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:41.3003188Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:41.3003532Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:41.3003789Z         
2025-03-14T05:12:41.3004226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:41.3004761Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:41.3005076Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:41.3005403Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:41.3005748Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:41.3006003Z         
2025-03-14T05:12:41.3006441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:41.3006960Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:41.3007286Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:41.3007616Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:41.3007866Z         
2025-03-14T05:12:41.3008288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:41.3008788Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:41.3009123Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:41.3009479Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:41.3009743Z         
2025-03-14T05:12:41.3010157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:41.3010639Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:41.3010905Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:41.3011139Z         
2025-03-14T05:12:41.3011532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:41.3011984Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:41.3012268Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:41.3012502Z         
2025-03-14T05:12:41.3012895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:41.3013369Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:41.3013680Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:41.3013929Z         
2025-03-14T05:12:41.3014324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:41.3014795Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:41.3015083Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:41.3015333Z         
2025-03-14T05:12:41.3015770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:41.3016354Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:41.3016647Z         
2025-03-14T05:12:41.3017070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:41.3017625Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:41.3017910Z         
2025-03-14T05:12:41.3018352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:41.3018967Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:41.3019352Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:41.3019659Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:41.3019962Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:41.3020282Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:41.3020543Z         
2025-03-14T05:12:41.3020929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.3021486Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3021831Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:41.3022073Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:41.3022442Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3022802Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:41.3023057Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:41.3023285Z         
2025-03-14T05:12:41.3023724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:41.3024393Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:12:41.3024703Z         
2025-03-14T05:12:41.3025161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:41.3025784Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:41.3026192Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:41.3026501Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:41.3026820Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:41.3027172Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:41.3027450Z         
2025-03-14T05:12:41.3028011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:41.3028723Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:41.3029073Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:41.3029425Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:41.3029775Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:41.3030079Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:41.3030327Z         
2025-03-14T05:12:41.3030779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:41.3031310Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:41.3031556Z         
2025-03-14T05:12:41.3031702Z 
2025-03-14T05:12:41.3031797Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:41.3033239Z     def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:41.3034612Z         l_stack0_ = L_stack0_
2025-03-14T05:12:41.3035012Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:12:41.3035588Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:12:41.3036159Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:12:41.3036728Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:12:41.3037215Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3037706Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3038260Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3038674Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3038960Z         
2025-03-14T05:12:41.3039481Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:12:41.3040143Z         mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:12:41.3040412Z         
2025-03-14T05:12:41.3040812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:12:41.3041810Z         scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:12:41.3042533Z         
2025-03-14T05:12:41.3042952Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:12:41.3043954Z         proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:12:41.3044683Z         
2025-03-14T05:12:41.3045060Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.3045518Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.3045775Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:41.3046011Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:41.3046292Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.3046560Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:41.3046808Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:41.3047059Z         
2025-03-14T05:12:41.3048013Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:41.3048956Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3049669Z         
2025-03-14T05:12:41.3050157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:41.3050729Z         deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:12:41.3051002Z         
2025-03-14T05:12:41.3051400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:41.3051919Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:41.3052201Z         
2025-03-14T05:12:41.3052605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:41.3053102Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:41.3053407Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.3053728Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:41.3054023Z         
2025-03-14T05:12:41.3054425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:41.3054922Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:41.3055219Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:41.3055556Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:41.3055826Z         
2025-03-14T05:12:41.3056221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:41.3056705Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.3056963Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:41.3057227Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:41.3057470Z         
2025-03-14T05:12:41.3057874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:41.3058378Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:41.3058798Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:41.3059153Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:41.3059710Z         
2025-03-14T05:12:41.3060193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:41.3060803Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:41.3061224Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:41.3061666Z         
2025-03-14T05:12:41.3062174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:41.3062961Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:41.3063354Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:41.3063699Z         
2025-03-14T05:12:41.3064260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:41.3064879Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:41.3065297Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:41.3065616Z         
2025-03-14T05:12:41.3066104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:41.3066727Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:41.3067226Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:41.3067577Z         
2025-03-14T05:12:41.3068069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:41.3068695Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:41.3069038Z         
2025-03-14T05:12:41.3069525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:41.3070165Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:41.3070488Z         
2025-03-14T05:12:41.3070977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:41.3071613Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:41.3072116Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:41.3072535Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:41.3072961Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:41.3073301Z         
2025-03-14T05:12:41.3073831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:41.3074449Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:41.3074830Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:41.3075251Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:41.3075665Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:41.3076005Z         
2025-03-14T05:12:41.3076593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:41.3077166Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:41.3077579Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:41.3077999Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:41.3078313Z         
2025-03-14T05:12:41.3078858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:41.3079457Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:41.3079893Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:41.3091908Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:41.3092286Z         
2025-03-14T05:12:41.3092765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:41.3093261Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:41.3093543Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:41.3093774Z         
2025-03-14T05:12:41.3094196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:41.3094679Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:41.3094953Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:41.3095205Z         
2025-03-14T05:12:41.3095618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:41.3096109Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:41.3096418Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:41.3096676Z         
2025-03-14T05:12:41.3097240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:41.3097715Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:41.3098003Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:41.3098252Z         
2025-03-14T05:12:41.3098737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:41.3099324Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:41.3099622Z         
2025-03-14T05:12:41.3100048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:41.3100622Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:41.3100919Z         
2025-03-14T05:12:41.3101376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:41.3101996Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:41.3102371Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:41.3102672Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:41.3102981Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:41.3103304Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:41.3103572Z         
2025-03-14T05:12:41.3104003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.3104702Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3105071Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:41.3105332Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:41.3105718Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3106087Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:41.3106349Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:41.3106588Z         
2025-03-14T05:12:41.3107019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:41.3107591Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:12:41.3107886Z         
2025-03-14T05:12:41.3108341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:41.3108957Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:41.3109321Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:41.3109619Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:41.3109924Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:41.3110245Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:41.3110512Z         
2025-03-14T05:12:41.3111091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:41.3111787Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:41.3112129Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:41.3112490Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:41.3112838Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:41.3113135Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:41.3113377Z         
2025-03-14T05:12:41.3113824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:41.3114350Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:41.3114581Z         
2025-03-14T05:12:41.3173599Z 
2025-03-14T05:12:41.3174705Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:41.3176441Z     def forward(self, L_stack0_: "f32[3233, 2048, 7, 7][100352, 49, 7, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 2048][2048, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:41.3184808Z         l_stack0_ = L_stack0_
2025-03-14T05:12:41.3189621Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:12:41.3218675Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:12:41.3221445Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:12:41.3222206Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:12:41.3222830Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3223372Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3223912Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3224514Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:41.3224948Z         
2025-03-14T05:12:41.3225643Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/roi_heads.py:480 in torch_dynamo_resume_in_forward_at_477, code: predictions = self.box_predictor(box_features.mean(dim=[2, 3]))
2025-03-14T05:12:41.3226470Z         mean: "f32[3233, 2048][2048, 1]cpu" = l_stack0_.mean(dim = [2, 3]);  l_stack0_ = None
2025-03-14T05:12:41.3226885Z         
2025-03-14T05:12:41.3231129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:12:41.3232386Z         scores: "f32[3233, 81][81, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:12:41.3233412Z         
2025-03-14T05:12:41.3233873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:12:41.3235009Z         proposal_deltas: "f32[3233, 320][320, 1]cpu" = torch._C._nn.linear(mean, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  mean = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:12:41.3235853Z         
2025-03-14T05:12:41.3236273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.3236769Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.3237040Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:41.3237287Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:41.3237577Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:41.3237851Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:41.3238111Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:41.3238342Z         
2025-03-14T05:12:41.3238729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:41.3239694Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3240454Z         
2025-03-14T05:12:41.3240969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:41.3241559Z         deltas: "f32[3233, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:12:41.3241851Z         
2025-03-14T05:12:41.3242253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:41.3242776Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:41.3243064Z         
2025-03-14T05:12:41.3243470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:41.3243973Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:41.3244283Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.3244604Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:41.3244874Z         
2025-03-14T05:12:41.3245274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:41.3245783Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:41.3246073Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:41.3246384Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:41.3246645Z         
2025-03-14T05:12:41.3247061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:41.3247543Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:41.3247798Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:41.3248058Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:41.3248300Z         
2025-03-14T05:12:41.3248717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:41.3249229Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:41.3249518Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:41.3249782Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:41.3250028Z         
2025-03-14T05:12:41.3250442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:41.3250961Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:41.3251282Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:41.3251516Z         
2025-03-14T05:12:41.3251897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:41.3252392Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:41.3252713Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:41.3252947Z         
2025-03-14T05:12:41.3253335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:41.3253861Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:41.3254252Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:41.3254489Z         
2025-03-14T05:12:41.3254879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:41.3255410Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:41.3255761Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:41.3256003Z         
2025-03-14T05:12:41.3256431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:41.3256969Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:41.3257237Z         
2025-03-14T05:12:41.3257662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:41.3258189Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:41.3258451Z         
2025-03-14T05:12:41.3258885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:41.3259426Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:41.3259750Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:41.3260105Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:41.3260457Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:41.3260716Z         
2025-03-14T05:12:41.3261151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:41.3261721Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:41.3262038Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:41.3262363Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:41.3262708Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:41.3262970Z         
2025-03-14T05:12:41.3263385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:41.3263894Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:41.3264348Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:41.3264709Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:41.3264973Z         
2025-03-14T05:12:41.3265410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:41.3265941Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:41.3266308Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:41.3266659Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:41.3266916Z         
2025-03-14T05:12:41.3267337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:41.3267819Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:41.3268092Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:41.3268332Z         
2025-03-14T05:12:41.3268733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:41.3269191Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:41.3269452Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:41.3269685Z         
2025-03-14T05:12:41.3270084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:41.3270563Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:41.3270855Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:41.3271105Z         
2025-03-14T05:12:41.3271505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:41.3271988Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:41.3272278Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:41.3272527Z         
2025-03-14T05:12:41.3272966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:41.3273573Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:41.3273876Z         
2025-03-14T05:12:41.3274299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:41.3274876Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:41.3275167Z         
2025-03-14T05:12:41.3275610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:41.3276219Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:41.3276582Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:41.3276870Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:41.3277175Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:41.3277497Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:41.3277760Z         
2025-03-14T05:12:41.3278143Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:41.3278704Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3279050Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:41.3279293Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:41.3279658Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:41.3280005Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:41.3280272Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:41.3280509Z         
2025-03-14T05:12:41.3280934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:41.3281669Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:12:41.3281973Z         
2025-03-14T05:12:41.3282425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:41.3283039Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:41.3283405Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:41.3283709Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:41.3284023Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:41.3284350Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:41.3284617Z         
2025-03-14T05:12:41.3285174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:41.3285869Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:41.3286213Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:41.3286555Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:41.3286952Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:41.3287250Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:41.3287491Z         
2025-03-14T05:12:41.3287931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:41.3288474Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:41.3288708Z         
2025-03-14T05:12:42.9776336Z 
2025-03-14T05:12:42.9778741Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:42.9779874Z     def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:42.9780774Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:12:42.9781025Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:12:42.9781370Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9781951Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9782385Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9782805Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9783121Z         
2025-03-14T05:12:42.9783570Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9784082Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9784815Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:42.9785160Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:42.9785474Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9785769Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:42.9786046Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:42.9786296Z         
2025-03-14T05:12:42.9786745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:42.9787819Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9788627Z         
2025-03-14T05:12:42.9789160Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:42.9789813Z         deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:12:42.9790119Z         
2025-03-14T05:12:42.9790584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:42.9791177Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:42.9791548Z         
2025-03-14T05:12:42.9792470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:42.9793152Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:42.9793502Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9793849Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:42.9794130Z         
2025-03-14T05:12:42.9794621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:42.9795148Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:42.9795457Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:42.9795789Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:42.9796068Z         
2025-03-14T05:12:42.9796491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:42.9797038Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9797416Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:42.9797693Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:42.9797939Z         
2025-03-14T05:12:42.9798368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:42.9798901Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:42.9799211Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:42.9799533Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:42.9799787Z         
2025-03-14T05:12:42.9800229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:42.9800800Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:42.9801184Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:42.9801429Z         
2025-03-14T05:12:42.9801915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:42.9802634Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:42.9802972Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:42.9803214Z         
2025-03-14T05:12:42.9803614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:42.9804142Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:42.9804471Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:42.9804713Z         
2025-03-14T05:12:42.9805113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:42.9805672Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:42.9806032Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:42.9806272Z         
2025-03-14T05:12:42.9806709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:42.9807279Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:42.9807543Z         
2025-03-14T05:12:42.9807978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:42.9808513Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:42.9808772Z         
2025-03-14T05:12:42.9809234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:42.9809793Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:42.9810120Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:42.9810463Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:42.9810823Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:42.9811089Z         
2025-03-14T05:12:42.9811539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:42.9812093Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:42.9812418Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:42.9812748Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:42.9813100Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:42.9813366Z         
2025-03-14T05:12:42.9813803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:42.9814337Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:42.9814689Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:42.9815042Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:42.9815300Z         
2025-03-14T05:12:42.9815735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:42.9816250Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:42.9816590Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:42.9816985Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:42.9817242Z         
2025-03-14T05:12:42.9817647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:42.9818114Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:42.9818380Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:42.9818617Z         
2025-03-14T05:12:42.9819014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:42.9819472Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:42.9819734Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:42.9819968Z         
2025-03-14T05:12:42.9820363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:42.9820918Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:42.9821217Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:42.9821470Z         
2025-03-14T05:12:42.9821874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:42.9822353Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:42.9822646Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:42.9822895Z         
2025-03-14T05:12:42.9823330Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:42.9823916Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:42.9824330Z         
2025-03-14T05:12:42.9824780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:42.9825368Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:42.9825676Z         
2025-03-14T05:12:42.9826156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:42.9826791Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:42.9827207Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:42.9827510Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:42.9827848Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:42.9828194Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:42.9828464Z         
2025-03-14T05:12:42.9828931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9829787Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9830322Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:42.9830613Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:42.9830987Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9831346Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:42.9831601Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:42.9831832Z         
2025-03-14T05:12:42.9832327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:42.9832952Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:12:42.9833291Z         
2025-03-14T05:12:42.9833745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:42.9834358Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:42.9834727Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:42.9835061Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:42.9835374Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:42.9835701Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:42.9835972Z         
2025-03-14T05:12:42.9836555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:42.9837279Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:42.9837634Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:42.9837983Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:42.9838337Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:42.9838642Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:42.9838893Z         
2025-03-14T05:12:42.9839349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:42.9839886Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:42.9840126Z         
2025-03-14T05:12:42.9840268Z 
2025-03-14T05:12:42.9840365Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:42.9841198Z     def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:42.9842028Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:12:42.9842288Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:12:42.9842608Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9843020Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9843421Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9843821Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9844119Z         
2025-03-14T05:12:42.9844510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9844997Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9845254Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:42.9845492Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:42.9845768Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9846029Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:42.9846278Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:42.9846503Z         
2025-03-14T05:12:42.9846876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:42.9847821Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9848551Z         
2025-03-14T05:12:42.9849016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:42.9849591Z         deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:12:42.9849877Z         
2025-03-14T05:12:42.9850302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:42.9850846Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:42.9851138Z         
2025-03-14T05:12:42.9851558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:42.9852081Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:42.9852404Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9852740Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:42.9853018Z         
2025-03-14T05:12:42.9853437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:42.9853966Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:42.9854269Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:42.9854598Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:42.9854876Z         
2025-03-14T05:12:42.9855298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:42.9855817Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9856111Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:42.9856370Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:42.9856615Z         
2025-03-14T05:12:42.9857024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:42.9857540Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:42.9857830Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:42.9858099Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:42.9858351Z         
2025-03-14T05:12:42.9858774Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:42.9859301Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:42.9859635Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:42.9859878Z         
2025-03-14T05:12:42.9860274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:42.9860790Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:42.9861118Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:42.9861356Z         
2025-03-14T05:12:42.9861752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:42.9862286Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:42.9862617Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:42.9862860Z         
2025-03-14T05:12:42.9863262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:42.9863824Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:42.9864303Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:42.9864583Z         
2025-03-14T05:12:42.9865064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:42.9865672Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:42.9865952Z         
2025-03-14T05:12:42.9866411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:42.9866951Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:42.9867213Z         
2025-03-14T05:12:42.9867659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:42.9868214Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:42.9868542Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:42.9868883Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:42.9869243Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:42.9869534Z         
2025-03-14T05:12:42.9870014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:42.9871150Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:42.9871535Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:42.9871877Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:42.9872234Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:42.9872560Z         
2025-03-14T05:12:42.9873212Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:42.9873826Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:42.9874152Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:42.9874499Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:42.9874758Z         
2025-03-14T05:12:42.9875191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:42.9875706Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:42.9876043Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:42.9876399Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:42.9876695Z         
2025-03-14T05:12:42.9877113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:42.9877577Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:42.9877841Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:42.9878076Z         
2025-03-14T05:12:42.9878495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:42.9878955Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:42.9879221Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:42.9879455Z         
2025-03-14T05:12:42.9879846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:42.9880323Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:42.9880619Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:42.9880870Z         
2025-03-14T05:12:42.9881261Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:42.9881904Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:42.9882196Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:42.9882447Z         
2025-03-14T05:12:42.9882878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:42.9883455Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:42.9883751Z         
2025-03-14T05:12:42.9884265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:42.9884864Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:42.9885156Z         
2025-03-14T05:12:42.9885604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:42.9886221Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:42.9886590Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:42.9886883Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:42.9887188Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:42.9887508Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:42.9887774Z         
2025-03-14T05:12:42.9888157Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9888714Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9889062Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:42.9889300Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:42.9889663Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9890015Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:42.9890313Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:42.9890534Z         
2025-03-14T05:12:42.9890958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:42.9891563Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:12:42.9892376Z         
2025-03-14T05:12:42.9892957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:42.9894241Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:42.9894633Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:42.9894946Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:42.9895278Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:42.9895616Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:42.9895893Z         
2025-03-14T05:12:42.9896480Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:42.9897220Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:42.9897584Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:42.9897936Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:42.9898296Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:42.9898610Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:42.9898913Z         
2025-03-14T05:12:42.9899406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:42.9899957Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:42.9900211Z         
2025-03-14T05:12:42.9900375Z 
2025-03-14T05:12:42.9900472Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:42.9901331Z     def forward(self, L_predictions_0_: "f32[3233, 81][81, 1]cpu", L_predictions_1_: "f32[3233, 320][320, 1]cpu", L_proposals_0_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", L_proposals_1_fields_proposal_boxes_tensor: "f32[1000, 4][4, 1]cpu", s0: "Sym(s0)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(1233 - s0)", L_proposals_3_fields_proposal_boxes_tensor: "f32[1233 - s0, 4][4, 1]cpu"):
2025-03-14T05:12:42.9902128Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:12:42.9902362Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:12:42.9902683Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9903086Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9903477Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9903873Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:12:42.9904231Z         
2025-03-14T05:12:42.9904649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9905146Z         size = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9905442Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:12:42.9905698Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:12:42.9905981Z         size_1 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:12:42.9906249Z         getitem_2: "Sym(1233 - s0)" = size_1[0]
2025-03-14T05:12:42.9906512Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:12:42.9906734Z         
2025-03-14T05:12:42.9907132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:12:42.9908078Z         proposal_boxes: "f32[3233, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0);  l_proposals_0_fields_proposal_boxes_tensor = l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9908792Z         
2025-03-14T05:12:42.9909255Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:12:42.9909838Z         deltas: "f32[3233, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:12:42.9910110Z         
2025-03-14T05:12:42.9910512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:12:42.9911041Z         boxes: "f32[3233, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:12:42.9911319Z         
2025-03-14T05:12:42.9911724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:12:42.9912226Z         getitem_4: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:12:42.9912529Z         getitem_5: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9912863Z         widths: "f32[3233][1]cpu" = getitem_4 - getitem_5;  getitem_4 = getitem_5 = None
2025-03-14T05:12:42.9913140Z         
2025-03-14T05:12:42.9913552Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:12:42.9914055Z         getitem_6: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:12:42.9914354Z         getitem_7: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:12:42.9914671Z         heights: "f32[3233][1]cpu" = getitem_6 - getitem_7;  getitem_6 = getitem_7 = None
2025-03-14T05:12:42.9914934Z         
2025-03-14T05:12:42.9915337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:12:42.9915819Z         getitem_8: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:12:42.9916080Z         mul: "f32[3233][1]cpu" = 0.5 * widths
2025-03-14T05:12:42.9916340Z         ctr_x: "f32[3233][1]cpu" = getitem_8 + mul;  getitem_8 = mul = None
2025-03-14T05:12:42.9916580Z         
2025-03-14T05:12:42.9916983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:12:42.9917487Z         getitem_9: "f32[3233][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:12:42.9917774Z         mul_1: "f32[3233][1]cpu" = 0.5 * heights
2025-03-14T05:12:42.9918044Z         ctr_y: "f32[3233][1]cpu" = getitem_9 + mul_1;  getitem_9 = mul_1 = None
2025-03-14T05:12:42.9918281Z         
2025-03-14T05:12:42.9921346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:12:42.9921878Z         getitem_10: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:12:42.9922214Z         dx: "f32[3233, 80][80, 1]cpu" = getitem_10 / 10.0;  getitem_10 = None
2025-03-14T05:12:42.9922457Z         
2025-03-14T05:12:42.9922851Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:12:42.9923385Z         getitem_11: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:12:42.9923715Z         dy: "f32[3233, 80][80, 1]cpu" = getitem_11 / 10.0;  getitem_11 = None
2025-03-14T05:12:42.9923954Z         
2025-03-14T05:12:42.9924340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:12:42.9924877Z         getitem_12: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:12:42.9925206Z         dw: "f32[3233, 80][80, 1]cpu" = getitem_12 / 5.0;  getitem_12 = None
2025-03-14T05:12:42.9925445Z         
2025-03-14T05:12:42.9925833Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:12:42.9926368Z         getitem_13: "f32[3233, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:12:42.9926712Z         dh: "f32[3233, 80][80, 1]cpu" = getitem_13 / 5.0;  getitem_13 = None
2025-03-14T05:12:42.9926944Z         
2025-03-14T05:12:42.9927367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:12:42.9927897Z         dw_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:12:42.9928154Z         
2025-03-14T05:12:42.9928611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:12:42.9929147Z         dh_1: "f32[3233, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:12:42.9929404Z         
2025-03-14T05:12:42.9929842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:12:42.9930377Z         getitem_14: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:12:42.9930696Z         mul_2: "f32[3233, 80][80, 1]cpu" = dx * getitem_14;  dx = getitem_14 = None
2025-03-14T05:12:42.9931031Z         getitem_15: "f32[3233, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:12:42.9931378Z         pred_ctr_x: "f32[3233, 80][80, 1]cpu" = mul_2 + getitem_15;  mul_2 = getitem_15 = None
2025-03-14T05:12:42.9931632Z         
2025-03-14T05:12:42.9932150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:12:42.9932702Z         getitem_16: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:12:42.9933023Z         mul_3: "f32[3233, 80][80, 1]cpu" = dy * getitem_16;  dy = getitem_16 = None
2025-03-14T05:12:42.9933358Z         getitem_17: "f32[3233, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:12:42.9933703Z         pred_ctr_y: "f32[3233, 80][80, 1]cpu" = mul_3 + getitem_17;  mul_3 = getitem_17 = None
2025-03-14T05:12:42.9933969Z         
2025-03-14T05:12:42.9934393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:12:42.9934993Z         exp: "f32[3233, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:12:42.9935322Z         getitem_18: "f32[3233, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:12:42.9935668Z         pred_w: "f32[3233, 80][80, 1]cpu" = exp * getitem_18;  exp = getitem_18 = None
2025-03-14T05:12:42.9935921Z         
2025-03-14T05:12:42.9936362Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:12:42.9936869Z         exp_1: "f32[3233, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:12:42.9937197Z         getitem_19: "f32[3233, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:12:42.9937550Z         pred_h: "f32[3233, 80][80, 1]cpu" = exp_1 * getitem_19;  exp_1 = getitem_19 = None
2025-03-14T05:12:42.9937807Z         
2025-03-14T05:12:42.9938218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:12:42.9938692Z         mul_6: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:12:42.9938960Z         x1: "f32[3233, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:12:42.9939197Z         
2025-03-14T05:12:42.9939598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:12:42.9940061Z         mul_7: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:12:42.9940325Z         y1: "f32[3233, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:12:42.9940563Z         
2025-03-14T05:12:42.9940960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:12:42.9941440Z         mul_8: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:12:42.9941756Z         x2: "f32[3233, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:12:42.9942018Z         
2025-03-14T05:12:42.9942408Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:12:42.9942877Z         mul_9: "f32[3233, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:12:42.9943163Z         y2: "f32[3233, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:12:42.9943409Z         
2025-03-14T05:12:42.9943840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:12:42.9944600Z         pred_boxes: "f32[3233, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:12:42.9944915Z         
2025-03-14T05:12:42.9945379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:12:42.9945961Z         predict_boxes: "f32[3233, 320][320, 1]cpu" = pred_boxes.reshape((3233, 320));  pred_boxes = None
2025-03-14T05:12:42.9946270Z         
2025-03-14T05:12:42.9946748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:12:42.9947411Z         split = predict_boxes.split([1000, 1000, getitem, getitem_2]);  predict_boxes = getitem = getitem_2 = None
2025-03-14T05:12:42.9947802Z         boxes_per_image: "f32[1000, 320][320, 1]cpu" = split[0]
2025-03-14T05:12:42.9948317Z         getitem_21: "f32[1000, 320][320, 1]cpu" = split[1];  getitem_21 = None
2025-03-14T05:12:42.9948637Z         getitem_22: "f32[s0, 320][320, 1]cpu" = split[2];  getitem_22 = None
2025-03-14T05:12:42.9948978Z         getitem_23: "f32[1233 - s0, 320][320, 1]cpu" = split[3];  split = getitem_23 = None
2025-03-14T05:12:42.9949263Z         
2025-03-14T05:12:42.9949669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:12:42.9950281Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9950657Z         getitem_24: "Sym(s0)" = size_2[0]
2025-03-14T05:12:42.9950922Z         getitem_25 = size_2[1];  size_2 = getitem_25 = None
2025-03-14T05:12:42.9951317Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:12:42.9951700Z         getitem_26: "Sym(1233 - s0)" = size_3[0]
2025-03-14T05:12:42.9951972Z         getitem_27 = size_3[1];  size_3 = getitem_27 = None
2025-03-14T05:12:42.9952211Z         
2025-03-14T05:12:42.9952670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:12:42.9953315Z         probs: "f32[3233, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:12:42.9953666Z         
2025-03-14T05:12:42.9954151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:12:42.9954797Z         split_1 = probs.split([1000, 1000, getitem_24, getitem_26], dim = 0);  probs = getitem_24 = getitem_26 = None
2025-03-14T05:12:42.9955210Z         scores_per_image: "f32[1000, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:12:42.9955510Z         getitem_29: "f32[1000, 81][81, 1]cpu" = split_1[1];  getitem_29 = None
2025-03-14T05:12:42.9955824Z         getitem_30: "f32[s0, 81][81, 1]cpu" = split_1[2];  getitem_30 = None
2025-03-14T05:12:42.9956178Z         getitem_31: "f32[1233 - s0, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_31 = None
2025-03-14T05:12:42.9956455Z         
2025-03-14T05:12:42.9957008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:42.9957707Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:12:42.9958051Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:42.9958391Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:12:42.9958734Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:42.9959031Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:42.9959273Z         
2025-03-14T05:12:42.9959713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:42.9960228Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:42.9960464Z         
2025-03-14T05:12:45.3794112Z 
2025-03-14T05:12:45.3799144Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:45.3800991Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:12:45.3801625Z         l_scores_0_ = L_scores_0_
2025-03-14T05:12:45.3801877Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:12:45.3802135Z         
2025-03-14T05:12:45.3802834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:45.3804088Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:12:45.3804490Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:45.3804900Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:12:45.3805363Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:45.3805666Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:45.3805915Z         
2025-03-14T05:12:45.3806377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:45.3806919Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:45.3807165Z         
2025-03-14T05:12:45.3807266Z 
2025-03-14T05:12:45.3807360Z class GraphModule(torch.nn.Module):
2025-03-14T05:12:45.3807682Z     def forward(self, L_scores_0_: "f32[1000, 81][81, 1]cpu", L_boxes_0_: "f32[1000, 320][320, 1]cpu"):
2025-03-14T05:12:45.3807989Z         l_scores_0_ = L_scores_0_
2025-03-14T05:12:45.3808208Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:12:45.3808409Z         
2025-03-14T05:12:45.3808969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:12:45.3809637Z         isfinite: "b8[1000, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:12:45.3809963Z         all_1: "b8[1000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:12:45.3810292Z         isfinite_1: "b8[1000, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:12:45.3810620Z         all_2: "b8[1000][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:12:45.3810971Z         valid_mask: "b8[1000][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:12:45.3811256Z         
2025-03-14T05:12:45.3811700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:12:45.3812224Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:12:45.3812462Z         
2025-03-14T05:13:17.9994496Z Compilation time (from dynamo_timed): 44.87914605
2025-03-14T05:13:17.9999013Z pass
2025-03-14T05:13:18.0001467Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:13:18.0002447Z TIMING: entire_frame_compile:44.87915 gc:0.05092 _recursive_pre_grad_passes:0.04783 async_compile.wait:15.81765 backend_compile:29.78361 _recursive_joint_graph_passes:0.17487 inductor_compile:19.31838 _recursive_post_grad_passes:0.06998 code_gen:18.25363 total_wall_time:44.87915
2025-03-14T05:13:18.0007259Z STATS: call_* op count: 766 | FakeTensorMode.__torch_dispatch__:15675 | FakeTensor.__torch_dispatch__:940 | ProxyTorchDispatchMode.__torch_dispatch__:3040 | attempt fast:161 | slow no contiguity match:24 | fast is_contiguous:133 | slow both tensors nontrivially broadcast:4
2025-03-14T05:13:18.0008320Z Dynamo produced 67 graphs covering 766 ops with 56 graph breaks (8 unique)
2025-03-14T05:13:24.1735969Z 
2025-03-14T05:13:32.0109178Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:13:32.0109692Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:13:32.0110114Z cpu  eval  detectron2_maskrcnn_r_50_fpn       
2025-03-14T05:13:46.1411576Z WARNING:common:fp64 golden ref were not generated for detectron2_maskrcnn_r_50_fpn. Setting accuracy check to cosine
2025-03-14T05:13:46.1627961Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:13:57.2785794Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:14:07.4566928Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:14:18.6052158Z 
2025-03-14T05:14:18.6052809Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:18.6133014Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:14:18.6199406Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:14:18.6199996Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6201091Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6202028Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6202921Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6203802Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6204673Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6205594Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6206574Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6207571Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6208506Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6209389Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6210304Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6211263Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6212176Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6213003Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6213784Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6214637Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6215498Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6216362Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6217172Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6217977Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.6218816Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6219709Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6220583Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.6221455Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.6222265Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6223078Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6223942Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6224920Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6225794Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6226633Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6227450Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6228345Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6229202Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6230036Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6230810Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6231621Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6232480Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6233320Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6234129Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6234909Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6235740Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6236629Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6237471Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6238277Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6239056Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6239864Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6240735Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6241569Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6242439Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6243216Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6244042Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6244905Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6245747Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6246558Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6247341Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6248146Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6249005Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6249861Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6250703Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6251481Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6252292Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6253151Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6253993Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6254810Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6255585Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6256444Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6257323Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6258175Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6258999Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6259805Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.6260662Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6261568Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6262450Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.6263328Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.6264270Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6265202Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6266180Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6267147Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6268066Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6268937Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6269855Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6270875Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6271846Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6272783Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6273667Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6274580Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6275507Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6276411Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6277327Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6278108Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6278938Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6279814Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6280650Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6281639Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6282433Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6283249Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6284110Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6284956Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6285818Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6286598Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6287428Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6288286Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6289130Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6289946Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6290725Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6291531Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6292811Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6293692Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6294511Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6295294Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6296109Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6296979Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6297827Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6298645Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6299454Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6300269Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6301155Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6301997Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6302812Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6303593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6304537Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6305458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6306319Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6307186Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6308031Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6308896Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6309804Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6310694Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6311549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6312382Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6313244Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6314168Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6314983Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6315783Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6316549Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.6317368Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6318234Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6319080Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.6319900Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.6320691Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6321502Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6322350Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6323195Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6324020Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6324803Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6325596Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6326437Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6327270Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6328061Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6328844Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6329641Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6330513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6331354Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6332172Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6332952Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6333770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6334658Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6335528Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6336339Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6337117Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6337926Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6338785Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6339618Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6340426Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6341223Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6342034Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6342934Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6343777Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6344657Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6345441Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6346249Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6347122Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6347962Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6348797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6349592Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6350415Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6351260Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6352090Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6352884Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6353648Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6354435Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6355298Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6356145Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6356942Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6357700Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6358493Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6359332Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6360148Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6360956Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6361748Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6362562Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6363424Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6364264Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6365079Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6365859Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6366671Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6367529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6368389Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6369203Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6370008Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6370814Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6371674Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6372512Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6373322Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6374102Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6374911Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6375795Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6376624Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6377408Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6378163Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6378948Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6379786Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6380606Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6381416Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6382350Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6383160Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6384066Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6384992Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6385868Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6386646Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6387453Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6388311Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6389187Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6390019Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6390802Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6391614Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6393458Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6394365Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6395186Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6395990Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.6396844Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6397818Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6398715Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.6399561Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.6400334Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6401144Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6402008Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6402842Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6403647Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6404452Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6405289Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6406162Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6407004Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6407814Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6408595Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6409403Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6410262Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6411129Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6411937Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6412730Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.6413533Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6414389Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6415222Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.6416031Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.6416809Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.6417614Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6418484Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6419335Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.6420150Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.6420932Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.6421750Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.6422614Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.6423452Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.6424328Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.6425019Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:14:18.6425553Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:14:18.6426073Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:14:18.6426574Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:14:18.6427068Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:14:18.6427567Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:14:18.6428060Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:14:18.6428546Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:14:18.6429034Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:14:18.6429528Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:14:18.6430015Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:14:18.6430499Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:14:18.6430994Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:14:18.6431500Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:14:18.6432011Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:14:18.6432502Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:14:18.6433124Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:14:18.6433884Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:14:18.6434636Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:14:18.6435388Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:14:18.6436133Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:14:18.6436867Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:14:18.6437559Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:14:18.6438320Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:14:18.6439108Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:14:18.6439897Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:14:18.6440649Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:14:18.6441121Z         
2025-03-14T05:14:18.6441527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6442403Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6443049Z         
2025-03-14T05:14:18.6443418Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6445438Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6447277Z         
2025-03-14T05:14:18.6447656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:14:18.6448132Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:14:18.6448394Z         
2025-03-14T05:14:18.6448841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:14:18.6449486Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:14:18.6449843Z         
2025-03-14T05:14:18.6450183Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6450994Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6451593Z         
2025-03-14T05:14:18.6451970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6454125Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6456038Z         
2025-03-14T05:14:18.6456420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6456908Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:14:18.6457167Z         
2025-03-14T05:14:18.6457512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6458327Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6458944Z         
2025-03-14T05:14:18.6459302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6461448Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6463381Z         
2025-03-14T05:14:18.6463759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6464336Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:14:18.6464625Z         
2025-03-14T05:14:18.6464994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6465887Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6466524Z         
2025-03-14T05:14:18.6466887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6468995Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6470880Z         
2025-03-14T05:14:18.6471220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6472033Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.6472650Z         
2025-03-14T05:14:18.6473002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6475230Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6477346Z         
2025-03-14T05:14:18.6477720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6478207Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:14:18.6478471Z         
2025-03-14T05:14:18.6478845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6479335Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:14:18.6479607Z         
2025-03-14T05:14:18.6479950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6480792Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6481398Z         
2025-03-14T05:14:18.6481936Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6484192Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6486192Z         
2025-03-14T05:14:18.6486587Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6487100Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:14:18.6487378Z         
2025-03-14T05:14:18.6487739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6488627Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6489288Z         
2025-03-14T05:14:18.6489663Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6491870Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6494149Z         
2025-03-14T05:14:18.6494529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6495014Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:14:18.6495322Z         
2025-03-14T05:14:18.6495662Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6496463Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6497081Z         
2025-03-14T05:14:18.6497456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6499561Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6501450Z         
2025-03-14T05:14:18.6501817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6502312Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:14:18.6502590Z         
2025-03-14T05:14:18.6502975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6503487Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:14:18.6503767Z         
2025-03-14T05:14:18.6504147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6505104Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6505755Z         
2025-03-14T05:14:18.6506109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6508221Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6510134Z         
2025-03-14T05:14:18.6510510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6510997Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:14:18.6511262Z         
2025-03-14T05:14:18.6511620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6512428Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6513043Z         
2025-03-14T05:14:18.6513397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6515514Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6517388Z         
2025-03-14T05:14:18.6517763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6518246Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:14:18.6518513Z         
2025-03-14T05:14:18.6518849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6519649Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6520262Z         
2025-03-14T05:14:18.6520619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6522731Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6524619Z         
2025-03-14T05:14:18.6524988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6525495Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:14:18.6525773Z         
2025-03-14T05:14:18.6526147Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6526659Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:14:18.6526951Z         
2025-03-14T05:14:18.6527316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6528127Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6528759Z         
2025-03-14T05:14:18.6529115Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6531348Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6533339Z         
2025-03-14T05:14:18.6533733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6534254Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:14:18.6534538Z         
2025-03-14T05:14:18.6534898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6535748Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6536393Z         
2025-03-14T05:14:18.6536768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6539010Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6540984Z         
2025-03-14T05:14:18.6541378Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6541884Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:14:18.6542164Z         
2025-03-14T05:14:18.6542531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6543425Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6544117Z         
2025-03-14T05:14:18.6544592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6546918Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6548970Z         
2025-03-14T05:14:18.6549329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6550183Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.6550841Z         
2025-03-14T05:14:18.6551213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6553511Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6555834Z         
2025-03-14T05:14:18.6556239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6556766Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:14:18.6557052Z         
2025-03-14T05:14:18.6557446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6557972Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:14:18.6558261Z         
2025-03-14T05:14:18.6558623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6559465Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6560098Z         
2025-03-14T05:14:18.6560468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6562730Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6564741Z         
2025-03-14T05:14:18.6565138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6565653Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:14:18.6565936Z         
2025-03-14T05:14:18.6566295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6567142Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6567809Z         
2025-03-14T05:14:18.6568192Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6570425Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6572417Z         
2025-03-14T05:14:18.6572808Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6573319Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:14:18.6573600Z         
2025-03-14T05:14:18.6573946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6574796Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6575464Z         
2025-03-14T05:14:18.6575855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6578110Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6580106Z         
2025-03-14T05:14:18.6580503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6581029Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:14:18.6581321Z         
2025-03-14T05:14:18.6581848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6582441Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:14:18.6582729Z         
2025-03-14T05:14:18.6583081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6583953Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6584653Z         
2025-03-14T05:14:18.6585035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6587263Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6589213Z         
2025-03-14T05:14:18.6589613Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6590125Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:14:18.6590437Z         
2025-03-14T05:14:18.6590820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6591670Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6592345Z         
2025-03-14T05:14:18.6593599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6595943Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6597981Z         
2025-03-14T05:14:18.6598385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6598903Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:14:18.6599181Z         
2025-03-14T05:14:18.6599539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6600412Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6601053Z         
2025-03-14T05:14:18.6601421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6603515Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6605389Z         
2025-03-14T05:14:18.6605772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6606292Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:14:18.6606575Z         
2025-03-14T05:14:18.6606956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6607459Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:14:18.6607733Z         
2025-03-14T05:14:18.6608073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6608873Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6609480Z         
2025-03-14T05:14:18.6609831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6611940Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6613830Z         
2025-03-14T05:14:18.6614226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6614716Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:14:18.6614983Z         
2025-03-14T05:14:18.6615322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6616126Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6616741Z         
2025-03-14T05:14:18.6617097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6619208Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6621089Z         
2025-03-14T05:14:18.6621459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6621944Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:14:18.6622209Z         
2025-03-14T05:14:18.6622546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6623342Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6623970Z         
2025-03-14T05:14:18.6624431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6626773Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6628897Z         
2025-03-14T05:14:18.6629301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6629855Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:14:18.6630164Z         
2025-03-14T05:14:18.6630578Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6631123Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:14:18.6631428Z         
2025-03-14T05:14:18.6631807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6632658Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6633251Z         
2025-03-14T05:14:18.6633600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6635701Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6637567Z         
2025-03-14T05:14:18.6637934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6638408Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:14:18.6638665Z         
2025-03-14T05:14:18.6639003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6639792Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6640414Z         
2025-03-14T05:14:18.6640767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6642858Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6644727Z         
2025-03-14T05:14:18.6645103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6645578Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:14:18.6645830Z         
2025-03-14T05:14:18.6646151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6646934Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6647518Z         
2025-03-14T05:14:18.6648455Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6650530Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6652340Z         
2025-03-14T05:14:18.6652676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6653453Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.6654049Z         
2025-03-14T05:14:18.6654392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6656501Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6658529Z         
2025-03-14T05:14:18.6658898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6659368Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:14:18.6659630Z         
2025-03-14T05:14:18.6660000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6660477Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:14:18.6660738Z         
2025-03-14T05:14:18.6661075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6661872Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6662471Z         
2025-03-14T05:14:18.6662813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6665031Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6666979Z         
2025-03-14T05:14:18.6667381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6667886Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:14:18.6668164Z         
2025-03-14T05:14:18.6668526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6669400Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6670041Z         
2025-03-14T05:14:18.6670432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6672658Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6674645Z         
2025-03-14T05:14:18.6675019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6675497Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:14:18.6675764Z         
2025-03-14T05:14:18.6676104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6676925Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6677562Z         
2025-03-14T05:14:18.6677914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6679993Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6682080Z         
2025-03-14T05:14:18.6682451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6682992Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:14:18.6683259Z         
2025-03-14T05:14:18.6683632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6684123Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:14:18.6684389Z         
2025-03-14T05:14:18.6684759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6685552Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6686149Z         
2025-03-14T05:14:18.6686497Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6688561Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6690389Z         
2025-03-14T05:14:18.6690814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6691304Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:14:18.6691559Z         
2025-03-14T05:14:18.6691902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6692701Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6693308Z         
2025-03-14T05:14:18.6693661Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6695732Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6697556Z         
2025-03-14T05:14:18.6697925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6698397Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:14:18.6698667Z         
2025-03-14T05:14:18.6698998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6699802Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6700408Z         
2025-03-14T05:14:18.6700759Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6702863Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6704906Z         
2025-03-14T05:14:18.6705316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6705825Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:14:18.6706099Z         
2025-03-14T05:14:18.6706460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6706934Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:14:18.6707197Z         
2025-03-14T05:14:18.6707535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6708317Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6708916Z         
2025-03-14T05:14:18.6709266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6711372Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6713302Z         
2025-03-14T05:14:18.6713681Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6714164Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:14:18.6714433Z         
2025-03-14T05:14:18.6714780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6715584Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6716208Z         
2025-03-14T05:14:18.6716567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6718701Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6720573Z         
2025-03-14T05:14:18.6720934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6721399Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:14:18.6721649Z         
2025-03-14T05:14:18.6721980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6722759Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6723346Z         
2025-03-14T05:14:18.6723691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6725782Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6727606Z         
2025-03-14T05:14:18.6727965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6728434Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:14:18.6728695Z         
2025-03-14T05:14:18.6729054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6729533Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:14:18.6729797Z         
2025-03-14T05:14:18.6730136Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6730947Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6731541Z         
2025-03-14T05:14:18.6731914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6733967Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6735790Z         
2025-03-14T05:14:18.6736156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6736623Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:14:18.6736878Z         
2025-03-14T05:14:18.6737205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6737980Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6738585Z         
2025-03-14T05:14:18.6738928Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6741013Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6742891Z         
2025-03-14T05:14:18.6743270Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6743752Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:14:18.6744017Z         
2025-03-14T05:14:18.6744443Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6745323Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6745979Z         
2025-03-14T05:14:18.6746336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6748446Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6750347Z         
2025-03-14T05:14:18.6750707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6751195Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:14:18.6751462Z         
2025-03-14T05:14:18.6751859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6752355Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:14:18.6752621Z         
2025-03-14T05:14:18.6752963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6753805Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6754400Z         
2025-03-14T05:14:18.6754751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6756860Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6758712Z         
2025-03-14T05:14:18.6759083Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6759577Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:14:18.6759850Z         
2025-03-14T05:14:18.6760184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6760980Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6761584Z         
2025-03-14T05:14:18.6761933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6764056Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6766014Z         
2025-03-14T05:14:18.6766394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6766882Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:14:18.6767142Z         
2025-03-14T05:14:18.6767494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6768318Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6768939Z         
2025-03-14T05:14:18.6769300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6771440Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6773355Z         
2025-03-14T05:14:18.6773750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6774268Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:14:18.6774540Z         
2025-03-14T05:14:18.6774921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6775418Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:14:18.6775682Z         
2025-03-14T05:14:18.6776019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6776807Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6777403Z         
2025-03-14T05:14:18.6777756Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6779845Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6781903Z         
2025-03-14T05:14:18.6782289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6782767Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:14:18.6783033Z         
2025-03-14T05:14:18.6783377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6784207Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6784819Z         
2025-03-14T05:14:18.6785173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6787327Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6789238Z         
2025-03-14T05:14:18.6789612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6790091Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:14:18.6790356Z         
2025-03-14T05:14:18.6790695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6791498Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6793158Z         
2025-03-14T05:14:18.6793529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6795707Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6797647Z         
2025-03-14T05:14:18.6797992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6798791Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.6799402Z         
2025-03-14T05:14:18.6799755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6801940Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6804033Z         
2025-03-14T05:14:18.6804404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6804884Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:14:18.6805145Z         
2025-03-14T05:14:18.6805521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6806007Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:14:18.6806274Z         
2025-03-14T05:14:18.6806614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6807405Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6807978Z         
2025-03-14T05:14:18.6808323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6810400Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6812216Z         
2025-03-14T05:14:18.6812577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6813041Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:14:18.6813293Z         
2025-03-14T05:14:18.6813619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6814388Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6814967Z         
2025-03-14T05:14:18.6817965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6820146Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6822043Z         
2025-03-14T05:14:18.6822419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6822903Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:14:18.6823155Z         
2025-03-14T05:14:18.6823494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6825658Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6826403Z         
2025-03-14T05:14:18.6826758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6829147Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6831044Z         
2025-03-14T05:14:18.6831426Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6831918Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:14:18.6832189Z         
2025-03-14T05:14:18.6832559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6833043Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:14:18.6833305Z         
2025-03-14T05:14:18.6833644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6834515Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.6835133Z         
2025-03-14T05:14:18.6835487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6837590Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6839461Z         
2025-03-14T05:14:18.6839828Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6840301Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:14:18.6840588Z         
2025-03-14T05:14:18.6840918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6841694Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.6842277Z         
2025-03-14T05:14:18.6842641Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6844703Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6846505Z         
2025-03-14T05:14:18.6846870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6847338Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:14:18.6847633Z         
2025-03-14T05:14:18.6847965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6848744Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.6849350Z         
2025-03-14T05:14:18.6849720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.6851752Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.6853584Z         
2025-03-14T05:14:18.6853948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.6854440Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:14:18.6854705Z         
2025-03-14T05:14:18.6855062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.6855536Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:14:18.6855791Z         
2025-03-14T05:14:18.6856129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6856990Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.6857655Z         
2025-03-14T05:14:18.6857985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6858807Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.6859451Z         
2025-03-14T05:14:18.6859950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.6860697Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:14:18.6861074Z         
2025-03-14T05:14:18.6861406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6862274Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.6862933Z         
2025-03-14T05:14:18.6863356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.6863934Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:14:18.6864318Z         
2025-03-14T05:14:18.6864659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6865550Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.6866246Z         
2025-03-14T05:14:18.6866743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.6867527Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:14:18.6867984Z         
2025-03-14T05:14:18.6868327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6869225Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.6869909Z         
2025-03-14T05:14:18.6870341Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.6870952Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:14:18.6871279Z         
2025-03-14T05:14:18.6871615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6872492Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.6873179Z         
2025-03-14T05:14:18.6873667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.6874455Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:14:18.6874899Z         
2025-03-14T05:14:18.6875259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6876154Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.6876851Z         
2025-03-14T05:14:18.6877283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.6877901Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:14:18.6878233Z         
2025-03-14T05:14:18.6878573Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6879514Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.6880256Z         
2025-03-14T05:14:18.6880700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:14:18.6881323Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:14:18.6881803Z         
2025-03-14T05:14:18.6882327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6883018Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.6883290Z         
2025-03-14T05:14:18.6883670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6884152Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:18.6884409Z         
2025-03-14T05:14:18.6884923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6885547Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.6885821Z         
2025-03-14T05:14:18.6886196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6886676Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:14:18.6886933Z         
2025-03-14T05:14:18.6887379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.6887970Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:14:18.6888340Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:14:18.6888614Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:14:18.6888885Z         
2025-03-14T05:14:18.6889298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.6889804Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:14:18.6890054Z         
2025-03-14T05:14:18.6890463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.6890957Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:14:18.6891203Z         
2025-03-14T05:14:18.6891667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.6892304Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:14:18.6892629Z         
2025-03-14T05:14:18.6893125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.6893705Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:14:18.6894296Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:14:18.6894903Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:14:18.6895203Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:14:18.6895441Z         
2025-03-14T05:14:18.6895945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6896574Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.6896838Z         
2025-03-14T05:14:18.6897216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6897742Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:14:18.6898003Z         
2025-03-14T05:14:18.6898510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6899124Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.6899387Z         
2025-03-14T05:14:18.6899758Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6900230Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:14:18.6900487Z         
2025-03-14T05:14:18.6900933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.6901551Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:14:18.6901900Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:14:18.6902174Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:14:18.6902433Z         
2025-03-14T05:14:18.6902842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.6903348Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:14:18.6903600Z         
2025-03-14T05:14:18.6903999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.6904577Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:14:18.6904837Z         
2025-03-14T05:14:18.6905320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.6905975Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:14:18.6906305Z         
2025-03-14T05:14:18.6906799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.6907381Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:14:18.6907974Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:14:18.6908579Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:14:18.6908874Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:14:18.6909104Z         
2025-03-14T05:14:18.6909610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6910283Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.6910547Z         
2025-03-14T05:14:18.6910924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6911400Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:14:18.6911659Z         
2025-03-14T05:14:18.6912161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6912773Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.6913033Z         
2025-03-14T05:14:18.6913404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6913876Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:14:18.6914132Z         
2025-03-14T05:14:18.6914580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.6915225Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:14:18.6915568Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:14:18.6915851Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:14:18.6916087Z         
2025-03-14T05:14:18.6916494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.6916994Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:14:18.6917235Z         
2025-03-14T05:14:18.6917638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.6918131Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:14:18.6918373Z         
2025-03-14T05:14:18.6918827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.6919455Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:14:18.6919781Z         
2025-03-14T05:14:18.6920263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.6920837Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:14:18.6921419Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:14:18.6922012Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:14:18.6922310Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:14:18.6922545Z         
2025-03-14T05:14:18.6923069Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6923673Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.6923930Z         
2025-03-14T05:14:18.6924300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6924775Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:14:18.6925030Z         
2025-03-14T05:14:18.6925530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6926136Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.6926397Z         
2025-03-14T05:14:18.6926767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6927236Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:14:18.6927490Z         
2025-03-14T05:14:18.6927954Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.6928556Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:14:18.6928897Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:14:18.6929182Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:14:18.6929416Z         
2025-03-14T05:14:18.6929822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.6930316Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:14:18.6930559Z         
2025-03-14T05:14:18.6930972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.6931462Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:14:18.6931699Z         
2025-03-14T05:14:18.6932152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.6932778Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:14:18.6933097Z         
2025-03-14T05:14:18.6933582Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.6934164Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:14:18.6934751Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:14:18.6935358Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:14:18.6935654Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:14:18.6935885Z         
2025-03-14T05:14:18.6936411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6937022Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.6937284Z         
2025-03-14T05:14:18.6937646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6938126Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:14:18.6938381Z         
2025-03-14T05:14:18.6938889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.6939497Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.6939759Z         
2025-03-14T05:14:18.6940131Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.6940606Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:14:18.6940860Z         
2025-03-14T05:14:18.6941333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.6941939Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:14:18.6942300Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:14:18.6942565Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:14:18.6942802Z         
2025-03-14T05:14:18.6943211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.6943706Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:14:18.6943951Z         
2025-03-14T05:14:18.6944436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.6944954Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:14:18.6945202Z         
2025-03-14T05:14:18.6945670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.6946307Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:14:18.6946639Z         
2025-03-14T05:14:18.6947138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.6947725Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:14:18.6948346Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:14:18.6948933Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:14:18.6949226Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:14:18.6949459Z         
2025-03-14T05:14:18.6949865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.6950352Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:14:18.6950669Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:14:18.6950976Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:14:18.6951282Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:14:18.6951580Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:14:18.6951827Z         
2025-03-14T05:14:18.6952179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6952691Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:14:18.6952766Z         
2025-03-14T05:14:18.6953046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.6953275Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:14:18.6953350Z         
2025-03-14T05:14:18.6953737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.6954281Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.6954346Z         
2025-03-14T05:14:18.6954720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.6955246Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:14:18.6955324Z         
2025-03-14T05:14:18.6955579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6956074Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:14:18.6956138Z         
2025-03-14T05:14:18.6956422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.6956637Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:14:18.6956712Z         
2025-03-14T05:14:18.6957084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.6957616Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.6957690Z         
2025-03-14T05:14:18.6958037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.6958556Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:14:18.6958625Z         
2025-03-14T05:14:18.6958883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6959335Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:14:18.6959408Z         
2025-03-14T05:14:18.6959667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.6959869Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:14:18.6959933Z         
2025-03-14T05:14:18.6960302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.6960799Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.6960870Z         
2025-03-14T05:14:18.6961220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.6961705Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:14:18.6961776Z         
2025-03-14T05:14:18.6962019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6962480Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:14:18.6962544Z         
2025-03-14T05:14:18.6962813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.6963008Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:14:18.6963079Z         
2025-03-14T05:14:18.6963441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.6963947Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.6964010Z         
2025-03-14T05:14:18.6964361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.6964852Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:14:18.6964917Z         
2025-03-14T05:14:18.6965168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.6965897Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:14:18.6965974Z         
2025-03-14T05:14:18.6966252Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.6966431Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:14:18.6966510Z         
2025-03-14T05:14:18.6966876Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.6967693Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:14:18.6967769Z         
2025-03-14T05:14:18.6968114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.6968892Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:14:18.6968965Z         
2025-03-14T05:14:18.6969288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:14:18.6969474Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:14:18.6969615Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:14:18.6969779Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:14:18.6969953Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:14:18.6970112Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:14:18.6970248Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:14:18.6970398Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:14:18.6970530Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:14:18.6970678Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:14:18.6970806Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:14:18.6970878Z         
2025-03-14T05:14:18.6971287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:14:18.6971468Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:14:18.6971653Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:14:18.6971845Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:14:18.6972014Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:14:18.6972197Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:14:18.6972375Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:14:18.6972521Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:14:18.6972688Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:14:18.6972850Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:14:18.6973002Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:14:18.6973156Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:14:18.6973329Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:14:18.6973464Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:14:18.6973627Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:14:18.6973785Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:14:18.6973857Z         
2025-03-14T05:14:18.6974246Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.6974473Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:14:18.6974537Z         
2025-03-14T05:14:18.6974966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.6975144Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:14:18.6975289Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:18.6975434Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:18.6975497Z         
2025-03-14T05:14:18.6975867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.6976034Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:18.6976104Z         
2025-03-14T05:14:18.6976406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.6976550Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:18.6976615Z         
2025-03-14T05:14:18.6976923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.6977052Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:18.6977185Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.6977348Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:18.6977417Z         
2025-03-14T05:14:18.6977728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.6977874Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:18.6977992Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:18.6978151Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:14:18.6978215Z         
2025-03-14T05:14:18.6978519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.6978642Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.6978742Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:18.6978868Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:14:18.6978941Z         
2025-03-14T05:14:18.6979245Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.6979396Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:18.6979487Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:18.6979624Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:14:18.6979688Z         
2025-03-14T05:14:18.6980021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.6980196Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.6980323Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:14:18.6980387Z         
2025-03-14T05:14:18.6980691Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.6980842Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.6980981Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:14:18.6981045Z         
2025-03-14T05:14:18.6981344Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.6981677Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.6981807Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:14:18.6981872Z         
2025-03-14T05:14:18.6982175Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.6982368Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:18.6982481Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:14:18.6982554Z         
2025-03-14T05:14:18.6982882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.6983030Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:18.6983095Z         
2025-03-14T05:14:18.6983473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.6983608Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:18.6983701Z         
2025-03-14T05:14:18.6984034Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.6984233Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:18.6984370Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:14:18.6984534Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:18.6984676Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:14:18.6984756Z         
2025-03-14T05:14:18.6985108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.6985261Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:18.6985396Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:14:18.6985554Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:18.6985689Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:14:18.6985765Z         
2025-03-14T05:14:18.6986093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.6986252Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:18.6986414Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:18.6986555Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:14:18.6986622Z         
2025-03-14T05:14:18.6986964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.6987106Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:18.6987283Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:18.6987419Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:14:18.6987495Z         
2025-03-14T05:14:18.6987812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.6987921Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:18.6988042Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:18.6988117Z         
2025-03-14T05:14:18.6988427Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.6988534Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:18.6988651Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:18.6988727Z         
2025-03-14T05:14:18.6989028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.6989170Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:18.6989300Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:18.6989375Z         
2025-03-14T05:14:18.6989676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.6989813Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:18.6989940Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:18.6990012Z         
2025-03-14T05:14:18.6990358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.6990548Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:18.6990622Z         
2025-03-14T05:14:18.6990958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.6991129Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:18.6991195Z         
2025-03-14T05:14:18.6991588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.6991764Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:18.6991837Z         
2025-03-14T05:14:18.6992724Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.6993006Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:18.6993074Z         
2025-03-14T05:14:18.6993521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.6993693Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:14:18.6993856Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:18.6993997Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:18.6994072Z         
2025-03-14T05:14:18.6994445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.6994626Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:18.6994694Z         
2025-03-14T05:14:18.6995012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.6995160Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:18.6995234Z         
2025-03-14T05:14:18.6995543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.6995686Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:18.6995815Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.6996014Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:14:18.6996081Z         
2025-03-14T05:14:18.6996404Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.6996545Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:18.6996678Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:18.6996832Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:14:18.6996908Z         
2025-03-14T05:14:18.6997220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.6997355Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.6997453Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:18.6997597Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:14:18.6997665Z         
2025-03-14T05:14:18.6997990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.6998142Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:18.6998247Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:18.6998378Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:14:18.6998451Z         
2025-03-14T05:14:18.6998767Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.6998943Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.6999075Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:14:18.6999141Z         
2025-03-14T05:14:18.6999441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.6999606Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.6999727Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:14:18.6999789Z         
2025-03-14T05:14:18.7000100Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7000246Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7000365Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:14:18.7000428Z         
2025-03-14T05:14:18.7000728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7000913Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:18.7001031Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:14:18.7001093Z         
2025-03-14T05:14:18.7001422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7001561Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:18.7001635Z         
2025-03-14T05:14:18.7001978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7002123Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:18.7002201Z         
2025-03-14T05:14:18.7002540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7002677Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.7002809Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:14:18.7002961Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:18.7003112Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:14:18.7003176Z         
2025-03-14T05:14:18.7003519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7003656Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.7003786Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:14:18.7003944Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:18.7004082Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:14:18.7004154Z         
2025-03-14T05:14:18.7004473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7004613Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:18.7004771Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:18.7004911Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:14:18.7004975Z         
2025-03-14T05:14:18.7005319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7005430Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:18.7005601Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:18.7005732Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:14:18.7005803Z         
2025-03-14T05:14:18.7006105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7006208Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:18.7006326Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:18.7006395Z         
2025-03-14T05:14:18.7006695Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7006798Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:18.7006912Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:18.7006983Z         
2025-03-14T05:14:18.7007278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7007416Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:18.7007547Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:18.7007635Z         
2025-03-14T05:14:18.7007932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7008056Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:18.7008186Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:18.7008261Z         
2025-03-14T05:14:18.7008595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7008798Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:18.7008864Z         
2025-03-14T05:14:18.7009198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7009365Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:18.7009441Z         
2025-03-14T05:14:18.7009814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7010021Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:18.7010088Z         
2025-03-14T05:14:18.7010499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7010703Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:18.7010778Z         
2025-03-14T05:14:18.7011208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7011368Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:14:18.7011524Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:18.7011658Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:18.7011730Z         
2025-03-14T05:14:18.7012096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7012267Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:14:18.7012331Z         
2025-03-14T05:14:18.7012644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7012786Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:18.7012856Z         
2025-03-14T05:14:18.7013163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7013296Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7013435Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7013586Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:14:18.7013674Z         
2025-03-14T05:14:18.7013992Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7014114Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7014236Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7014382Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:14:18.7014454Z         
2025-03-14T05:14:18.7014751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7014878Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7014970Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:18.7015105Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:14:18.7015168Z         
2025-03-14T05:14:18.7015476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7015622Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:18.7015721Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:18.7015846Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:14:18.7015916Z         
2025-03-14T05:14:18.7016230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7016387Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7016499Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:14:18.7016571Z         
2025-03-14T05:14:18.7016868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7017045Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7017156Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:14:18.7017228Z         
2025-03-14T05:14:18.7017519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7017673Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7017790Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:14:18.7017856Z         
2025-03-14T05:14:18.7018156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7018334Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:18.7018449Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:14:18.7018512Z         
2025-03-14T05:14:18.7018848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7019005Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:18.7019079Z         
2025-03-14T05:14:18.7019412Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7019570Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:18.7019635Z         
2025-03-14T05:14:18.7019994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7020128Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.7020263Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:14:18.7020417Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:18.7020570Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:14:18.7020635Z         
2025-03-14T05:14:18.7020989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7021128Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.7021258Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:14:18.7021407Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:18.7021553Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:14:18.7021619Z         
2025-03-14T05:14:18.7021975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7022092Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:18.7022266Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:18.7022400Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:14:18.7022474Z         
2025-03-14T05:14:18.7022823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7022946Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:18.7023114Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:18.7023257Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:14:18.7023322Z         
2025-03-14T05:14:18.7023640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7023744Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:18.7023867Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:18.7023933Z         
2025-03-14T05:14:18.7024325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7024432Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:18.7024561Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:18.7024628Z         
2025-03-14T05:14:18.7024970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7025094Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:18.7025255Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:18.7025325Z         
2025-03-14T05:14:18.7025658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7025784Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:18.7025913Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:18.7025989Z         
2025-03-14T05:14:18.7026339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7026542Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:18.7026609Z         
2025-03-14T05:14:18.7026953Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7027116Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:18.7027190Z         
2025-03-14T05:14:18.7027569Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7027752Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:18.7027873Z         
2025-03-14T05:14:18.7028279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7028484Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:14:18.7028557Z         
2025-03-14T05:14:18.7029003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7029161Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:14:18.7029310Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:18.7029456Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:18.7029525Z         
2025-03-14T05:14:18.7029911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7030081Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:14:18.7030156Z         
2025-03-14T05:14:18.7030472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7030626Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:18.7030691Z         
2025-03-14T05:14:18.7031016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7031168Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7031304Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7031453Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:14:18.7031544Z         
2025-03-14T05:14:18.7031860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7031993Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7032116Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7032274Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:14:18.7032340Z         
2025-03-14T05:14:18.7032659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7032792Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7032885Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:18.7033022Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:14:18.7033087Z         
2025-03-14T05:14:18.7033405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7033552Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:18.7033652Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:18.7033783Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:14:18.7033872Z         
2025-03-14T05:14:18.7034184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7034347Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7034462Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:14:18.7034535Z         
2025-03-14T05:14:18.7034853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7035011Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7035121Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:14:18.7035196Z         
2025-03-14T05:14:18.7035490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7035645Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7035757Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:14:18.7035830Z         
2025-03-14T05:14:18.7036125Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7036318Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:18.7036428Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:14:18.7036504Z         
2025-03-14T05:14:18.7036834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7037006Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:18.7037074Z         
2025-03-14T05:14:18.7037410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7037564Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:18.7037637Z         
2025-03-14T05:14:18.7037977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7038121Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.7038248Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:14:18.7038411Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:18.7038558Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:14:18.7038624Z         
2025-03-14T05:14:18.7038971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7039109Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.7039240Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:14:18.7039391Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:18.7039539Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:14:18.7039626Z         
2025-03-14T05:14:18.7039963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7040079Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:18.7040249Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:18.7040383Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:14:18.7040470Z         
2025-03-14T05:14:18.7040802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7040923Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:18.7041088Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:18.7041231Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:14:18.7041298Z         
2025-03-14T05:14:18.7041612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7041713Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:18.7041838Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:18.7041905Z         
2025-03-14T05:14:18.7042218Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7042313Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:18.7042436Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:18.7042504Z         
2025-03-14T05:14:18.7042832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7042949Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:18.7043103Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:18.7043171Z         
2025-03-14T05:14:18.7043485Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7043602Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:18.7043741Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:18.7043808Z         
2025-03-14T05:14:18.7044161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7044362Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:18.7044442Z         
2025-03-14T05:14:18.7044766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7044933Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:18.7045000Z         
2025-03-14T05:14:18.7045385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7045559Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:18.7045649Z         
2025-03-14T05:14:18.7046045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7046261Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:14:18.7046334Z         
2025-03-14T05:14:18.7046775Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7046931Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:14:18.7047079Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:18.7047222Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:18.7047288Z         
2025-03-14T05:14:18.7047668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7047833Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:14:18.7047906Z         
2025-03-14T05:14:18.7048216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7048362Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:18.7048428Z         
2025-03-14T05:14:18.7048760Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7048890Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7049021Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7049182Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:14:18.7049254Z         
2025-03-14T05:14:18.7049571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7049701Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7049818Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7049981Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:14:18.7050050Z         
2025-03-14T05:14:18.7050364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7050485Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7050582Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:18.7050712Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:14:18.7050785Z         
2025-03-14T05:14:18.7051092Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7051246Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:18.7051339Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:18.7051473Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:14:18.7051558Z         
2025-03-14T05:14:18.7051870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7052026Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7052148Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:14:18.7052214Z         
2025-03-14T05:14:18.7052959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7053111Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7053231Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:14:18.7053299Z         
2025-03-14T05:14:18.7053608Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7053764Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7053875Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:14:18.7053949Z         
2025-03-14T05:14:18.7054251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7054439Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:18.7054548Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:14:18.7054622Z         
2025-03-14T05:14:18.7054972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7055121Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:18.7055186Z         
2025-03-14T05:14:18.7055532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7055667Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:18.7055744Z         
2025-03-14T05:14:18.7056081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7056225Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.7056350Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:14:18.7056512Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:18.7056647Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:14:18.7056720Z         
2025-03-14T05:14:18.7057062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7057204Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.7057324Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:14:18.7057482Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:18.7057616Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:14:18.7057703Z         
2025-03-14T05:14:18.7058036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7058160Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:18.7058319Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:18.7058497Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:14:18.7058564Z         
2025-03-14T05:14:18.7058903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7059016Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:18.7059192Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:18.7059323Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:14:18.7059397Z         
2025-03-14T05:14:18.7059704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7059810Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:18.7059928Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:18.7060004Z         
2025-03-14T05:14:18.7060308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7060409Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:18.7060521Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:18.7060597Z         
2025-03-14T05:14:18.7060914Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7061053Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:18.7061190Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:18.7061255Z         
2025-03-14T05:14:18.7061566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7061679Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:18.7061812Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:18.7061878Z         
2025-03-14T05:14:18.7062231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7062416Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:18.7062490Z         
2025-03-14T05:14:18.7062820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7062986Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:18.7063051Z         
2025-03-14T05:14:18.7063437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7063625Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:18.7063697Z         
2025-03-14T05:14:18.7064247Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:18.7064409Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:18.7064478Z         
2025-03-14T05:14:18.7064807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7064953Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:14:18.7065028Z         
2025-03-14T05:14:18.7065473Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7065600Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:14:18.7065705Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:18.7065830Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:18.7065896Z         
2025-03-14T05:14:18.7066359Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7066493Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7066728Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:14:18.7066796Z         
2025-03-14T05:14:18.7067279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7067464Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7067539Z         
2025-03-14T05:14:18.7067839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7067971Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:18.7068038Z         
2025-03-14T05:14:18.7068478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7068611Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:14:18.7068723Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:18.7068854Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:18.7068923Z         
2025-03-14T05:14:18.7069389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7069525Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7069780Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:14:18.7090634Z         
2025-03-14T05:14:18.7091345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7091693Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7091792Z         
2025-03-14T05:14:18.7092139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7092328Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:18.7092431Z         
2025-03-14T05:14:18.7092890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7093026Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:14:18.7093151Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:18.7093298Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:18.7093370Z         
2025-03-14T05:14:18.7093872Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7094030Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7094304Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:14:18.7094375Z         
2025-03-14T05:14:18.7094902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7095087Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7095184Z         
2025-03-14T05:14:18.7095532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7095682Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:18.7095754Z         
2025-03-14T05:14:18.7096207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7096325Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:14:18.7096444Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:18.7096569Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:18.7096643Z         
2025-03-14T05:14:18.7097101Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7097251Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7097493Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:14:18.7097573Z         
2025-03-14T05:14:18.7098032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7098220Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7098295Z         
2025-03-14T05:14:18.7098601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7098742Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:18.7098813Z         
2025-03-14T05:14:18.7099278Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7099398Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:14:18.7099516Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:18.7099635Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:18.7099713Z         
2025-03-14T05:14:18.7100171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7100350Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:18.7100586Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:14:18.7100663Z         
2025-03-14T05:14:18.7101120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7101298Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7101384Z         
2025-03-14T05:14:18.7101693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7101836Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:18.7101913Z         
2025-03-14T05:14:18.7102198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7102586Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:18.7102654Z         
2025-03-14T05:14:18.7102946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7103412Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:18.7103491Z         
2025-03-14T05:14:18.7103778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7103987Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:14:18.7104063Z         
2025-03-14T05:14:18.7104551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:18.7104737Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:18.7104806Z         
2025-03-14T05:14:18.7105124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.7105284Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:14:18.7105363Z         
2025-03-14T05:14:18.7105776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:18.7105928Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:18.7105998Z         
2025-03-14T05:14:18.7106513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:18.7106662Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:14:18.7106801Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:18.7106964Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:18.7107112Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:18.7107182Z         
2025-03-14T05:14:18.7107572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:18.7107696Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:18.7107773Z         
2025-03-14T05:14:18.7108361Z 
2025-03-14T05:14:18.7108463Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:18.7173825Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", 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L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:14:18.7174437Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:14:18.7174880Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7175369Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7175866Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7176338Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7176781Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7177212Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7177680Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7178185Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7178653Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7179510Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7179964Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7180485Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7181035Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7181711Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7182173Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7182620Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7183134Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7183696Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7184156Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7184685Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7185182Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7185708Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7186203Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7186687Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7187154Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7187559Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7188059Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7188519Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7189004Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7189433Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7189849Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7190319Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7190772Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7191211Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7191656Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7192081Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7192519Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7192987Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7193434Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7193862Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7194268Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7194712Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7195188Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7195610Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7196051Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7196435Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7196873Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7197327Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7197747Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7198169Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7198567Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7199022Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7199498Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7199933Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7200348Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7200729Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7201189Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7201640Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7202053Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7202499Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7202899Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7203376Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7203819Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7204260Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7204672Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7205057Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7205505Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7205960Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7206380Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7206806Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7207210Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7207664Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7208126Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7208567Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7208987Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7209367Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7209821Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7210283Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7210697Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7211126Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7211515Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7211964Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7212409Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7212843Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7213259Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7213654Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7214102Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7214546Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7214966Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7215383Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7215760Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7216205Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7216659Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7217082Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7217506Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7217886Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7218333Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7218769Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7219195Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7219598Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7220001Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7220448Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7220903Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7221323Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7221732Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7222119Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7222560Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7223004Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7223424Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7223850Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7224369Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7224918Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7225438Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7225865Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7226319Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7226746Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7227230Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7227744Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7228212Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7228670Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7229081Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7229572Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7230064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7230529Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7230978Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7231414Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7231899Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7232401Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7232856Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7233310Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7233721Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7234207Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7234639Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7235081Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7235495Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7235909Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7236369Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7236816Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7237250Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7237674Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7238055Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7238501Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7238957Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7239378Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7239797Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7240184Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7240626Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7241067Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7241488Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7241888Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7242291Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7242731Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7243193Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7243603Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7244017Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7244399Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7244843Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7245285Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7245718Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7246129Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7246526Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7246978Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7247421Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7247837Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7248254Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7248634Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7249105Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7249544Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7249988Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7250410Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7250787Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7251234Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7251670Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7252094Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7252500Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7252902Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7253356Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7253811Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7254233Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7254642Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7255028Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7255487Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7255927Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7256363Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7256768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7257172Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7257613Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7258063Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7258475Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7258889Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7259274Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7259715Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7260169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7260597Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7261011Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7261394Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7261842Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7262286Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7262702Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7263116Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7263513Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7263975Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7264473Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7264895Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7265313Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7265692Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7266146Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7266582Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7267030Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7267445Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7267877Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7268327Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7268762Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7269183Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7269597Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7269978Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7270444Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7270880Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7271313Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7271715Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7272100Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7272541Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7272985Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7273407Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7273811Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7274221Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7274663Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7275122Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7275537Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7275953Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7276352Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7276804Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7277264Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7277720Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7278172Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7278564Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7279016Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7279479Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7279902Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7280316Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7280695Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7281177Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7281831Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7282305Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7282721Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7283109Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7283564Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7283974Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7284373Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7284791Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7285163Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7285616Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7286017Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7286401Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7286773Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7287131Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7287533Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7287939Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7288359Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7288734Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7289101Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7289508Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7289919Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7290311Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7290686Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7290925Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:14:18.7291145Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:14:18.7291389Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:14:18.7291601Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:14:18.7291845Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:14:18.7292059Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:14:18.7292282Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:14:18.7292489Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:14:18.7292724Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:14:18.7292935Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:14:18.7293162Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:14:18.7293368Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:14:18.7293596Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:14:18.7293807Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:14:18.7294033Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:14:18.7294273Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:14:18.7294628Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:14:18.7294988Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:14:18.7295352Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:14:18.7295707Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:14:18.7296052Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:14:18.7296381Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:14:18.7296696Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:14:18.7297068Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:14:18.7297437Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:14:18.7297808Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:14:18.7298169Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:14:18.7298237Z         
2025-03-14T05:14:18.7298535Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7299082Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7299163Z         
2025-03-14T05:14:18.7299444Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7301179Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7301273Z         
2025-03-14T05:14:18.7301567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:14:18.7301715Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:14:18.7301779Z         
2025-03-14T05:14:18.7302162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:14:18.7302399Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:14:18.7302473Z         
2025-03-14T05:14:18.7302729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7303217Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7303283Z         
2025-03-14T05:14:18.7303560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7305539Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7305645Z         
2025-03-14T05:14:18.7305976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7306124Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:14:18.7306214Z         
2025-03-14T05:14:18.7306468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7306969Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7307044Z         
2025-03-14T05:14:18.7307310Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7309131Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7309226Z         
2025-03-14T05:14:18.7309514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7309666Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:14:18.7309733Z         
2025-03-14T05:14:18.7309989Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7310494Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7310568Z         
2025-03-14T05:14:18.7310831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7312622Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7312711Z         
2025-03-14T05:14:18.7312967Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7313472Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7313541Z         
2025-03-14T05:14:18.7313816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7315668Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7315752Z         
2025-03-14T05:14:18.7316044Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7316192Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:14:18.7316266Z         
2025-03-14T05:14:18.7316555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7316713Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:14:18.7316780Z         
2025-03-14T05:14:18.7317039Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7317525Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7317597Z         
2025-03-14T05:14:18.7317879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7319645Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7319737Z         
2025-03-14T05:14:18.7320026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7320177Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:14:18.7320244Z         
2025-03-14T05:14:18.7320499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7320996Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7321080Z         
2025-03-14T05:14:18.7321357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7323137Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7323214Z         
2025-03-14T05:14:18.7323507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7323659Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:14:18.7323732Z         
2025-03-14T05:14:18.7323974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7324471Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7324537Z         
2025-03-14T05:14:18.7324809Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7326594Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7326673Z         
2025-03-14T05:14:18.7326960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7327114Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:14:18.7327187Z         
2025-03-14T05:14:18.7327467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7327625Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:14:18.7327707Z         
2025-03-14T05:14:18.7327969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7328449Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7328524Z         
2025-03-14T05:14:18.7328804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7330564Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7330639Z         
2025-03-14T05:14:18.7330923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7331096Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:14:18.7331163Z         
2025-03-14T05:14:18.7331421Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7331921Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7331997Z         
2025-03-14T05:14:18.7332262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7334040Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7334132Z         
2025-03-14T05:14:18.7334414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7334578Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:14:18.7334642Z         
2025-03-14T05:14:18.7334896Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7335407Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7335473Z         
2025-03-14T05:14:18.7335739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7337513Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7337590Z         
2025-03-14T05:14:18.7337889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7338045Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:14:18.7338130Z         
2025-03-14T05:14:18.7338410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7338571Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:14:18.7338637Z         
2025-03-14T05:14:18.7338892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7339374Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7339447Z         
2025-03-14T05:14:18.7339706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7341546Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7341644Z         
2025-03-14T05:14:18.7341959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7342133Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:14:18.7342202Z         
2025-03-14T05:14:18.7342483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7343003Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7343081Z         
2025-03-14T05:14:18.7343367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7345379Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7345488Z         
2025-03-14T05:14:18.7345800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7345960Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:14:18.7346028Z         
2025-03-14T05:14:18.7346305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7346797Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7346873Z         
2025-03-14T05:14:18.7347132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7348906Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7349658Z         
2025-03-14T05:14:18.7349911Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7350415Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7350488Z         
2025-03-14T05:14:18.7350750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7352605Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7352691Z         
2025-03-14T05:14:18.7352974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7353135Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:14:18.7353201Z         
2025-03-14T05:14:18.7353495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7353648Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:14:18.7353723Z         
2025-03-14T05:14:18.7353972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7354472Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7354538Z         
2025-03-14T05:14:18.7354812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7356608Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7356709Z         
2025-03-14T05:14:18.7357000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7357150Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:14:18.7357225Z         
2025-03-14T05:14:18.7357471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7357972Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7358038Z         
2025-03-14T05:14:18.7358314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7360129Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7360221Z         
2025-03-14T05:14:18.7360523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7360668Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:14:18.7360744Z         
2025-03-14T05:14:18.7360999Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7361503Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7361569Z         
2025-03-14T05:14:18.7361846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7363648Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7363717Z         
2025-03-14T05:14:18.7364006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7364161Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:14:18.7364235Z         
2025-03-14T05:14:18.7364513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7364674Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:14:18.7364740Z         
2025-03-14T05:14:18.7364995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7365506Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7365588Z         
2025-03-14T05:14:18.7365859Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7367657Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7367733Z         
2025-03-14T05:14:18.7368027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7368169Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:14:18.7368240Z         
2025-03-14T05:14:18.7368488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7368996Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7369062Z         
2025-03-14T05:14:18.7369332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7371109Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7371189Z         
2025-03-14T05:14:18.7371479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7371621Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:14:18.7371694Z         
2025-03-14T05:14:18.7371958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7372462Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7372541Z         
2025-03-14T05:14:18.7372817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7374612Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7374681Z         
2025-03-14T05:14:18.7374970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7375125Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:14:18.7375257Z         
2025-03-14T05:14:18.7375543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7375704Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:14:18.7375775Z         
2025-03-14T05:14:18.7376033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7376544Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7376617Z         
2025-03-14T05:14:18.7376894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7378704Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7378781Z         
2025-03-14T05:14:18.7379067Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7379230Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:14:18.7379295Z         
2025-03-14T05:14:18.7379558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7380059Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7380125Z         
2025-03-14T05:14:18.7380400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7382392Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7382529Z         
2025-03-14T05:14:18.7382825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7382966Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:14:18.7383044Z         
2025-03-14T05:14:18.7383318Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7383818Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7383887Z         
2025-03-14T05:14:18.7384156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7386039Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7386140Z         
2025-03-14T05:14:18.7386429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7386584Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:14:18.7386657Z         
2025-03-14T05:14:18.7386939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7387094Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:14:18.7387161Z         
2025-03-14T05:14:18.7387419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7387909Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7387991Z         
2025-03-14T05:14:18.7388283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7390163Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7390257Z         
2025-03-14T05:14:18.7390561Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7390720Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:14:18.7390789Z         
2025-03-14T05:14:18.7391066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7391588Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7391672Z         
2025-03-14T05:14:18.7391960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7393870Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7393967Z         
2025-03-14T05:14:18.7394267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7394421Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:14:18.7394490Z         
2025-03-14T05:14:18.7394761Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7395287Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7395355Z         
2025-03-14T05:14:18.7395636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7397494Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7397571Z         
2025-03-14T05:14:18.7397830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7398317Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7398392Z         
2025-03-14T05:14:18.7398656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7400523Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7400614Z         
2025-03-14T05:14:18.7400894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7401043Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:14:18.7401111Z         
2025-03-14T05:14:18.7401401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7401546Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:14:18.7401619Z         
2025-03-14T05:14:18.7401871Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7402348Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7402434Z         
2025-03-14T05:14:18.7402706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7404493Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7404562Z         
2025-03-14T05:14:18.7404855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7404992Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:14:18.7405067Z         
2025-03-14T05:14:18.7405319Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7405809Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7405879Z         
2025-03-14T05:14:18.7406162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7407937Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7408031Z         
2025-03-14T05:14:18.7408326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7408459Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:14:18.7408534Z         
2025-03-14T05:14:18.7408784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7409276Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7409391Z         
2025-03-14T05:14:18.7409652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7411433Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7411511Z         
2025-03-14T05:14:18.7411786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7411945Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:14:18.7412010Z         
2025-03-14T05:14:18.7412297Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7412439Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:14:18.7412512Z         
2025-03-14T05:14:18.7412773Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7413256Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7413336Z         
2025-03-14T05:14:18.7413611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7415374Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7415451Z         
2025-03-14T05:14:18.7415743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7415895Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:14:18.7415968Z         
2025-03-14T05:14:18.7416215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7416714Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7416782Z         
2025-03-14T05:14:18.7417058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7418834Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7418905Z         
2025-03-14T05:14:18.7419198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7419352Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:14:18.7419428Z         
2025-03-14T05:14:18.7419678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7420196Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7420262Z         
2025-03-14T05:14:18.7420532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7422304Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7422390Z         
2025-03-14T05:14:18.7422683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7422829Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:14:18.7422905Z         
2025-03-14T05:14:18.7423187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7423349Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:14:18.7423414Z         
2025-03-14T05:14:18.7423670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7424161Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7424297Z         
2025-03-14T05:14:18.7424597Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7426489Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7426588Z         
2025-03-14T05:14:18.7426905Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7427046Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:14:18.7427124Z         
2025-03-14T05:14:18.7427389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7428899Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7428998Z         
2025-03-14T05:14:18.7429299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7431189Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7431304Z         
2025-03-14T05:14:18.7431638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7431782Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:14:18.7431859Z         
2025-03-14T05:14:18.7432120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7432642Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7432712Z         
2025-03-14T05:14:18.7432997Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7434893Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7434980Z         
2025-03-14T05:14:18.7435288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7435441Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:14:18.7435521Z         
2025-03-14T05:14:18.7435820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7435978Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:14:18.7436050Z         
2025-03-14T05:14:18.7436323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7436800Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7436874Z         
2025-03-14T05:14:18.7437133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7438928Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7439006Z         
2025-03-14T05:14:18.7439288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7439430Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:14:18.7439496Z         
2025-03-14T05:14:18.7439749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7440235Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7440300Z         
2025-03-14T05:14:18.7440564Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7442328Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7442419Z         
2025-03-14T05:14:18.7442710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7442844Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:14:18.7442916Z         
2025-03-14T05:14:18.7443165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7443655Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7443735Z         
2025-03-14T05:14:18.7444008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7445778Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7445855Z         
2025-03-14T05:14:18.7446140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7446286Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:14:18.7446358Z         
2025-03-14T05:14:18.7446638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7446786Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:14:18.7446852Z         
2025-03-14T05:14:18.7447105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7447590Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7447680Z         
2025-03-14T05:14:18.7447943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7449703Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7449784Z         
2025-03-14T05:14:18.7450065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7450208Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:14:18.7450271Z         
2025-03-14T05:14:18.7450544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7451026Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7451105Z         
2025-03-14T05:14:18.7451382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7453145Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7453225Z         
2025-03-14T05:14:18.7453506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7453648Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:14:18.7453713Z         
2025-03-14T05:14:18.7453985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7454477Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7454562Z         
2025-03-14T05:14:18.7454831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7456592Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7456668Z         
2025-03-14T05:14:18.7456951Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7457116Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:14:18.7457188Z         
2025-03-14T05:14:18.7457465Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7457627Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:14:18.7457691Z         
2025-03-14T05:14:18.7457964Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7458431Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7458505Z         
2025-03-14T05:14:18.7458765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7460555Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7460632Z         
2025-03-14T05:14:18.7460926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7461066Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:14:18.7461131Z         
2025-03-14T05:14:18.7461387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7461865Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7461939Z         
2025-03-14T05:14:18.7462198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7464003Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7464099Z         
2025-03-14T05:14:18.7464461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7464630Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:14:18.7464701Z         
2025-03-14T05:14:18.7464974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7465489Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7465568Z         
2025-03-14T05:14:18.7465845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7467750Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7467850Z         
2025-03-14T05:14:18.7468116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7468641Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7468719Z         
2025-03-14T05:14:18.7469000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7470948Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7471042Z         
2025-03-14T05:14:18.7471338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7471495Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:14:18.7471562Z         
2025-03-14T05:14:18.7471882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7472033Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:14:18.7472108Z         
2025-03-14T05:14:18.7472371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7472879Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7472949Z         
2025-03-14T05:14:18.7473233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7475100Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7475205Z         
2025-03-14T05:14:18.7475493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7475625Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:14:18.7475699Z         
2025-03-14T05:14:18.7475950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7476429Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7476496Z         
2025-03-14T05:14:18.7476766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7478567Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7478650Z         
2025-03-14T05:14:18.7478942Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7479073Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:14:18.7479148Z         
2025-03-14T05:14:18.7479400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7479898Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7479965Z         
2025-03-14T05:14:18.7480236Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7482226Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7482366Z         
2025-03-14T05:14:18.7482654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7482818Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:14:18.7482898Z         
2025-03-14T05:14:18.7483195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7483360Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:14:18.7483429Z         
2025-03-14T05:14:18.7483702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7484213Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7484313Z         
2025-03-14T05:14:18.7484586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7486375Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7486456Z         
2025-03-14T05:14:18.7486749Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7486887Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:14:18.7486960Z         
2025-03-14T05:14:18.7487208Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7487699Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7487767Z         
2025-03-14T05:14:18.7488035Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7489805Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7489879Z         
2025-03-14T05:14:18.7490171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7490314Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:14:18.7490388Z         
2025-03-14T05:14:18.7490638Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7491134Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7491220Z         
2025-03-14T05:14:18.7491489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7493284Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7493352Z         
2025-03-14T05:14:18.7493640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7493790Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:14:18.7493863Z         
2025-03-14T05:14:18.7494141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7494305Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:14:18.7494370Z         
2025-03-14T05:14:18.7494626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7495207Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.7495281Z         
2025-03-14T05:14:18.7495527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7496078Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.7496152Z         
2025-03-14T05:14:18.7496563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.7496841Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:14:18.7496906Z         
2025-03-14T05:14:18.7497161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7497719Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.7497807Z         
2025-03-14T05:14:18.7498150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.7498365Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:14:18.7498431Z         
2025-03-14T05:14:18.7498686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7499243Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.7499316Z         
2025-03-14T05:14:18.7499722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.7500040Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:14:18.7500114Z         
2025-03-14T05:14:18.7500361Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7500947Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.7501026Z         
2025-03-14T05:14:18.7501372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.7501582Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:14:18.7501655Z         
2025-03-14T05:14:18.7501898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7502480Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.7502555Z         
2025-03-14T05:14:18.7502949Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.7503281Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:14:18.7503347Z         
2025-03-14T05:14:18.7503600Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7504251Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.7504338Z         
2025-03-14T05:14:18.7504719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.7504948Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:14:18.7505016Z         
2025-03-14T05:14:18.7505284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7505935Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.7506014Z         
2025-03-14T05:14:18.7506373Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:14:18.7506610Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:14:18.7506690Z         
2025-03-14T05:14:18.7507163Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7507336Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.7507406Z         
2025-03-14T05:14:18.7507750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7507901Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:18.7507978Z         
2025-03-14T05:14:18.7508428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7508597Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.7508668Z         
2025-03-14T05:14:18.7508980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7509128Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:14:18.7509208Z         
2025-03-14T05:14:18.7509596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.7509792Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:14:18.7509900Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:14:18.7510035Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:14:18.7510104Z         
2025-03-14T05:14:18.7510456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.7510612Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:14:18.7510685Z         
2025-03-14T05:14:18.7511024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.7511164Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:14:18.7511232Z         
2025-03-14T05:14:18.7511651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.7511876Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:14:18.7511955Z         
2025-03-14T05:14:18.7512395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.7512535Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:14:18.7512986Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:14:18.7513125Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:14:18.7513258Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:14:18.7513326Z         
2025-03-14T05:14:18.7513806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7513965Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.7514042Z         
2025-03-14T05:14:18.7514365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7514522Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:14:18.7514592Z         
2025-03-14T05:14:18.7515058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7515203Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.7515278Z         
2025-03-14T05:14:18.7515566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7515711Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:14:18.7515777Z         
2025-03-14T05:14:18.7516151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.7516350Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:14:18.7516463Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:14:18.7516587Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:14:18.7516658Z         
2025-03-14T05:14:18.7516983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.7517135Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:14:18.7517200Z         
2025-03-14T05:14:18.7517528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.7517654Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:14:18.7517739Z         
2025-03-14T05:14:18.7518118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.7518340Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:14:18.7518407Z         
2025-03-14T05:14:18.7518825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.7518959Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:14:18.7519381Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:14:18.7519513Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:14:18.7519631Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:14:18.7519702Z         
2025-03-14T05:14:18.7520142Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7520301Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.7520381Z         
2025-03-14T05:14:18.7520678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7520817Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:14:18.7520891Z         
2025-03-14T05:14:18.7521316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7521469Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.7521538Z         
2025-03-14T05:14:18.7521835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7521975Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:14:18.7522051Z         
2025-03-14T05:14:18.7522420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.7522622Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:14:18.7522727Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:14:18.7522858Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:14:18.7522940Z         
2025-03-14T05:14:18.7523273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.7523401Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:14:18.7523477Z         
2025-03-14T05:14:18.7523803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.7523948Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:14:18.7524014Z         
2025-03-14T05:14:18.7524395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.7524603Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:14:18.7524680Z         
2025-03-14T05:14:18.7525086Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.7525221Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:14:18.7525635Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:14:18.7525765Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:14:18.7525886Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:14:18.7525952Z         
2025-03-14T05:14:18.7526392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7526536Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.7526622Z         
2025-03-14T05:14:18.7526915Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7527061Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:14:18.7527126Z         
2025-03-14T05:14:18.7527563Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7527708Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.7527781Z         
2025-03-14T05:14:18.7528072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7528211Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:14:18.7528276Z         
2025-03-14T05:14:18.7528659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.7528850Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:14:18.7528974Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:14:18.7530075Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:14:18.7530203Z         
2025-03-14T05:14:18.7530782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.7530926Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:14:18.7530995Z         
2025-03-14T05:14:18.7531328Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.7531472Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:14:18.7531549Z         
2025-03-14T05:14:18.7531927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.7532151Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:14:18.7532218Z         
2025-03-14T05:14:18.7532635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.7532763Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:14:18.7533181Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:14:18.7533309Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:14:18.7533423Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:14:18.7533497Z         
2025-03-14T05:14:18.7533947Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7534116Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.7534183Z         
2025-03-14T05:14:18.7534487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7534623Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:14:18.7534697Z         
2025-03-14T05:14:18.7535124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.7535275Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.7535341Z         
2025-03-14T05:14:18.7535640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7535777Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:14:18.7535851Z         
2025-03-14T05:14:18.7536224Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.7536422Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:14:18.7536524Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:14:18.7536665Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:14:18.7536731Z         
2025-03-14T05:14:18.7537062Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.7537188Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:14:18.7537261Z         
2025-03-14T05:14:18.7537594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.7537724Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:14:18.7537789Z         
2025-03-14T05:14:18.7538167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.7538377Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:14:18.7538451Z         
2025-03-14T05:14:18.7538855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.7538986Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:14:18.7539398Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:14:18.7539526Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:14:18.7539649Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:14:18.7539727Z         
2025-03-14T05:14:18.7540033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.7540180Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:14:18.7540323Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:14:18.7540448Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:14:18.7540579Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:14:18.7540699Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:14:18.7540772Z         
2025-03-14T05:14:18.7541028Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7541540Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:14:18.7541607Z         
2025-03-14T05:14:18.7541887Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.7542086Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:14:18.7542161Z         
2025-03-14T05:14:18.7542541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.7543105Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.7543172Z         
2025-03-14T05:14:18.7543541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.7544078Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:14:18.7544154Z         
2025-03-14T05:14:18.7544456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7544969Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:14:18.7545044Z         
2025-03-14T05:14:18.7545325Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.7545542Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:14:18.7545606Z         
2025-03-14T05:14:18.7545987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.7546524Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.7546616Z         
2025-03-14T05:14:18.7546969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.7547491Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:14:18.7547570Z         
2025-03-14T05:14:18.7547830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7548308Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:14:18.7548374Z         
2025-03-14T05:14:18.7548651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.7548836Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:14:18.7548909Z         
2025-03-14T05:14:18.7549279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.7549797Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.7549864Z         
2025-03-14T05:14:18.7550221Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.7550730Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:14:18.7550803Z         
2025-03-14T05:14:18.7551056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7551527Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:14:18.7551594Z         
2025-03-14T05:14:18.7551873Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.7552060Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:14:18.7552126Z         
2025-03-14T05:14:18.7552504Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.7553010Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.7553097Z         
2025-03-14T05:14:18.7553453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.7553962Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:14:18.7554027Z         
2025-03-14T05:14:18.7554290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7555051Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:14:18.7555126Z         
2025-03-14T05:14:18.7555407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.7555580Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:14:18.7555668Z         
2025-03-14T05:14:18.7556038Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.7556924Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:14:18.7556992Z         
2025-03-14T05:14:18.7557355Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.7558166Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:14:18.7558239Z         
2025-03-14T05:14:18.7558583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:14:18.7558748Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:14:18.7558898Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:14:18.7559083Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:14:18.7559236Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:14:18.7559390Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:14:18.7559550Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:14:18.7559696Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:14:18.7559835Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:14:18.7559980Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:14:18.7560119Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:14:18.7560187Z         
2025-03-14T05:14:18.7560617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:14:18.7560795Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:14:18.7560983Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:14:18.7561165Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:14:18.7561337Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:14:18.7561513Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:14:18.7561711Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:14:18.7561861Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:14:18.7562037Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:14:18.7562205Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:14:18.7562372Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:14:18.7562544Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:14:18.7562712Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:14:18.7562859Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:14:18.7563012Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:14:18.7563185Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:14:18.7563250Z         
2025-03-14T05:14:18.7563660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7563859Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:14:18.7563935Z         
2025-03-14T05:14:18.7564380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7564548Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:14:18.7564697Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:18.7564858Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:18.7564924Z         
2025-03-14T05:14:18.7565307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7565477Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:18.7565549Z         
2025-03-14T05:14:18.7565858Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7566011Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:18.7566078Z         
2025-03-14T05:14:18.7566395Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7566529Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7566666Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7566817Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:18.7566888Z         
2025-03-14T05:14:18.7567205Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7567357Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7567477Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7567637Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:14:18.7567702Z         
2025-03-14T05:14:18.7568017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7568155Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7568255Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:18.7568383Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:14:18.7568457Z         
2025-03-14T05:14:18.7568766Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7568924Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:18.7569015Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:18.7569155Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:14:18.7569220Z         
2025-03-14T05:14:18.7569567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7569734Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7569849Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:14:18.7569921Z         
2025-03-14T05:14:18.7570223Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7570400Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7570514Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:14:18.7570603Z         
2025-03-14T05:14:18.7570901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7571063Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7571174Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:14:18.7571249Z         
2025-03-14T05:14:18.7571554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7571751Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:18.7571866Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:14:18.7571944Z         
2025-03-14T05:14:18.7572282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7572436Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:18.7572505Z         
2025-03-14T05:14:18.7572846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7572987Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:18.7573065Z         
2025-03-14T05:14:18.7573431Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7573580Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:18.7573707Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:14:18.7573867Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:18.7574022Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:14:18.7574094Z         
2025-03-14T05:14:18.7574437Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7574583Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:18.7574709Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:14:18.7574867Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:18.7575001Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:14:18.7575074Z         
2025-03-14T05:14:18.7575402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7575530Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:18.7575699Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:18.7575830Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:14:18.7575908Z         
2025-03-14T05:14:18.7576254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7576381Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:18.7576563Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:18.7576708Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:14:18.7576774Z         
2025-03-14T05:14:18.7577091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7577191Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:18.7577316Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:18.7577384Z         
2025-03-14T05:14:18.7577700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7577794Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:18.7577917Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:18.7577983Z         
2025-03-14T05:14:18.7578289Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7578407Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:18.7578546Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:18.7578611Z         
2025-03-14T05:14:18.7578919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7579060Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:18.7579194Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:18.7579260Z         
2025-03-14T05:14:18.7579611Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7579806Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:18.7579881Z         
2025-03-14T05:14:18.7580214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7580386Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:18.7580452Z         
2025-03-14T05:14:18.7580841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7581018Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:18.7581092Z         
2025-03-14T05:14:18.7581738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7581970Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:18.7582037Z         
2025-03-14T05:14:18.7582478Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7582719Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:14:18.7582879Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:18.7583044Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:18.7583118Z         
2025-03-14T05:14:18.7583488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7583665Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:18.7583740Z         
2025-03-14T05:14:18.7584046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7584245Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:18.7584317Z         
2025-03-14T05:14:18.7584636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7584775Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7584917Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7585071Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:14:18.7585145Z         
2025-03-14T05:14:18.7585476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7585641Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7585764Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7585923Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:14:18.7585990Z         
2025-03-14T05:14:18.7586303Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7586451Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7586556Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:18.7586689Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:14:18.7586762Z         
2025-03-14T05:14:18.7587072Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7587233Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:18.7587328Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:18.7587465Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:14:18.7587529Z         
2025-03-14T05:14:18.7587840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7587993Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7588113Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:14:18.7588179Z         
2025-03-14T05:14:18.7588486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7588650Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7588774Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:14:18.7588852Z         
2025-03-14T05:14:18.7589152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7589301Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7589421Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:14:18.7589486Z         
2025-03-14T05:14:18.7589789Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7589977Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:18.7590097Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:14:18.7590163Z         
2025-03-14T05:14:18.7590503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7590650Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:18.7590717Z         
2025-03-14T05:14:18.7591048Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7591186Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:18.7591283Z         
2025-03-14T05:14:18.7591625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7591770Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.7591901Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:14:18.7592066Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:18.7592225Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:14:18.7592298Z         
2025-03-14T05:14:18.7592644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7592791Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.7592920Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:14:18.7593080Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:18.7593223Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:14:18.7593296Z         
2025-03-14T05:14:18.7593624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7593749Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:18.7593909Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:18.7594053Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:14:18.7594119Z         
2025-03-14T05:14:18.7594468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7594597Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:18.7594772Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:18.7594906Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:14:18.7594981Z         
2025-03-14T05:14:18.7595286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7595393Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:18.7595512Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:18.7595587Z         
2025-03-14T05:14:18.7595890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7595996Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:18.7596113Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:18.7596186Z         
2025-03-14T05:14:18.7596487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7596614Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:18.7596747Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:18.7596821Z         
2025-03-14T05:14:18.7597120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7597261Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:18.7597391Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:18.7597465Z         
2025-03-14T05:14:18.7597806Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7598019Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:18.7598090Z         
2025-03-14T05:14:18.7598425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7598602Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:18.7598666Z         
2025-03-14T05:14:18.7599056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7599235Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:18.7599306Z         
2025-03-14T05:14:18.7599702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7599915Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:18.7599980Z         
2025-03-14T05:14:18.7600441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7600591Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:14:18.7600783Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:18.7600920Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:18.7600992Z         
2025-03-14T05:14:18.7601348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7601518Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:14:18.7601584Z         
2025-03-14T05:14:18.7601891Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7602030Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:18.7602103Z         
2025-03-14T05:14:18.7602406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7602540Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7602659Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7602811Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:14:18.7602873Z         
2025-03-14T05:14:18.7603188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7603322Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7603446Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7603592Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:14:18.7603664Z         
2025-03-14T05:14:18.7603972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7604097Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7604187Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:18.7604324Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:14:18.7604387Z         
2025-03-14T05:14:18.7604701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7604844Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:18.7604944Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:18.7605071Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:14:18.7605142Z         
2025-03-14T05:14:18.7605451Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7605603Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7605721Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:14:18.7605788Z         
2025-03-14T05:14:18.7606105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7606256Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7606391Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:14:18.7606456Z         
2025-03-14T05:14:18.7606753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7606902Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7607020Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:14:18.7607085Z         
2025-03-14T05:14:18.7607394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7607578Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:18.7607693Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:14:18.7607762Z         
2025-03-14T05:14:18.7608104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7608245Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:18.7608319Z         
2025-03-14T05:14:18.7608654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7608796Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:18.7608882Z         
2025-03-14T05:14:18.7609219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7609353Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.7609483Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:14:18.7609647Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:18.7609792Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:14:18.7609855Z         
2025-03-14T05:14:18.7610198Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7610332Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.7610471Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:14:18.7610627Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:18.7610760Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:14:18.7610832Z         
2025-03-14T05:14:18.7611151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7611270Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:18.7611422Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:18.7611578Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:14:18.7611641Z         
2025-03-14T05:14:18.7611969Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7612094Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:18.7612260Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:18.7612389Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:14:18.7612460Z         
2025-03-14T05:14:18.7612754Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7612857Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:18.7612974Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:18.7613046Z         
2025-03-14T05:14:18.7613338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7613442Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:18.7613553Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:18.7613625Z         
2025-03-14T05:14:18.7613916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7614036Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:18.7614164Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:18.7614249Z         
2025-03-14T05:14:18.7614544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7614666Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:18.7614794Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:18.7614867Z         
2025-03-14T05:14:18.7615213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7615409Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:18.7615473Z         
2025-03-14T05:14:18.7615802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7615960Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:18.7616030Z         
2025-03-14T05:14:18.7616397Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7616575Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:18.7616641Z         
2025-03-14T05:14:18.7617036Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7617235Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:14:18.7617310Z         
2025-03-14T05:14:18.7617748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7617917Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:14:18.7618071Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:18.7618208Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:18.7618280Z         
2025-03-14T05:14:18.7618644Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7618818Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:14:18.7618885Z         
2025-03-14T05:14:18.7619200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7619346Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:18.7619419Z         
2025-03-14T05:14:18.7619727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7619862Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7619986Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7620141Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:14:18.7620222Z         
2025-03-14T05:14:18.7620550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7620673Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7620803Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7620955Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:14:18.7621029Z         
2025-03-14T05:14:18.7621352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7621484Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7621576Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:18.7621717Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:14:18.7621785Z         
2025-03-14T05:14:18.7622098Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7622246Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:18.7622349Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:18.7622481Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:14:18.7622555Z         
2025-03-14T05:14:18.7622855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7623016Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7623127Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:14:18.7623202Z         
2025-03-14T05:14:18.7623528Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7623705Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7623817Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:14:18.7623892Z         
2025-03-14T05:14:18.7624267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7624442Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7624556Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:14:18.7624635Z         
2025-03-14T05:14:18.7624956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7625145Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:18.7625269Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:14:18.7625336Z         
2025-03-14T05:14:18.7625688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7625840Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:18.7625916Z         
2025-03-14T05:14:18.7626244Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7626410Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:18.7626475Z         
2025-03-14T05:14:18.7626822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7626957Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.7627087Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:14:18.7627253Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:18.7627401Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:14:18.7627465Z         
2025-03-14T05:14:18.7627816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7627952Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.7628085Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:14:18.7628237Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:18.7628381Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:14:18.7628448Z         
2025-03-14T05:14:18.7628783Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7628896Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:18.7629063Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:18.7629212Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:14:18.7629285Z         
2025-03-14T05:14:18.7629609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7629747Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:18.7629912Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:18.7630051Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:14:18.7630117Z         
2025-03-14T05:14:18.7630428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7630526Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:18.7630652Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:18.7630716Z         
2025-03-14T05:14:18.7631025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7631120Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:18.7631243Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:18.7631309Z         
2025-03-14T05:14:18.7631615Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7631731Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:18.7631870Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:18.7631947Z         
2025-03-14T05:14:18.7632259Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7632374Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:18.7632510Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:18.7632575Z         
2025-03-14T05:14:18.7632941Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7633135Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:18.7633201Z         
2025-03-14T05:14:18.7633539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7633701Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:18.7633774Z         
2025-03-14T05:14:18.7634152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7634333Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:18.7634400Z         
2025-03-14T05:14:18.7634800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.7635007Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:14:18.7635095Z         
2025-03-14T05:14:18.7635524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.7635694Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:14:18.7635840Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:18.7635985Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:18.7636051Z         
2025-03-14T05:14:18.7636428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.7636592Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:14:18.7636666Z         
2025-03-14T05:14:18.7636965Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.7637111Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:18.7637176Z         
2025-03-14T05:14:18.7637483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.7637608Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:18.7637738Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7637878Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:14:18.7637966Z         
2025-03-14T05:14:18.7638279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.7638407Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:18.7638525Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:18.7638676Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:14:18.7638739Z         
2025-03-14T05:14:18.7639063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.7639179Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.7639276Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:18.7639406Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:14:18.7639479Z         
2025-03-14T05:14:18.7639778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.7639927Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:18.7640024Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:18.7640149Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:14:18.7640221Z         
2025-03-14T05:14:18.7640514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.7640668Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.7640796Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:14:18.7640867Z         
2025-03-14T05:14:18.7641159Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.7641325Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.7641440Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:14:18.7641511Z         
2025-03-14T05:14:18.7641798Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.7641947Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.7642054Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:14:18.7642127Z         
2025-03-14T05:14:18.7642419Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.7642604Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:18.7642713Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:14:18.7642787Z         
2025-03-14T05:14:18.7643106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.7643249Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:18.7643316Z         
2025-03-14T05:14:18.7643642Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.7643787Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:18.7643861Z         
2025-03-14T05:14:18.7644191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.7644333Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.7644476Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:14:18.7644635Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:18.7644768Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:14:18.7644841Z         
2025-03-14T05:14:18.7645177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.7645315Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.7645439Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:14:18.7645583Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:18.7645721Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:14:18.7645786Z         
2025-03-14T05:14:18.7646112Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.7646223Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:18.7646399Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:18.7646528Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:14:18.7646597Z         
2025-03-14T05:14:18.7646932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.7647050Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:18.7647212Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:18.7647349Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:14:18.7647413Z         
2025-03-14T05:14:18.7647719Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.7647817Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:18.7647935Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:18.7647998Z         
2025-03-14T05:14:18.7648305Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.7648401Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:18.7648521Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:18.7648584Z         
2025-03-14T05:14:18.7648883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.7649020Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:18.7649171Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:18.7649236Z         
2025-03-14T05:14:18.7649538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.7649651Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:18.7649785Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:18.7649849Z         
2025-03-14T05:14:18.7650204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.7650387Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:18.7650462Z         
2025-03-14T05:14:18.7650780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.7650942Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:18.7651008Z         
2025-03-14T05:14:18.7651381Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.7651547Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:18.7651620Z         
2025-03-14T05:14:18.7652082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:18.7652242Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:18.7652307Z         
2025-03-14T05:14:18.7652603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7652757Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:14:18.7652828Z         
2025-03-14T05:14:18.7653253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7653373Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:14:18.7653482Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:18.7653594Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:18.7653666Z         
2025-03-14T05:14:18.7654114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7654252Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7654477Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:14:18.7654547Z         
2025-03-14T05:14:18.7654987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7655156Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7655237Z         
2025-03-14T05:14:18.7655537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7655657Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:18.7655730Z         
2025-03-14T05:14:18.7656149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7656285Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:14:18.7656394Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:18.7656519Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:18.7656582Z         
2025-03-14T05:14:18.7657033Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7657163Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7657400Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:14:18.7657465Z         
2025-03-14T05:14:18.7657926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7658090Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7658165Z         
2025-03-14T05:14:18.7658475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7658613Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:18.7658679Z         
2025-03-14T05:14:18.7659124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7659238Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:14:18.7659352Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:18.7659469Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:18.7659543Z         
2025-03-14T05:14:18.7660002Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7660135Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7660376Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:14:18.7660443Z         
2025-03-14T05:14:18.7660897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7661058Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7661131Z         
2025-03-14T05:14:18.7661417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7661565Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:18.7661631Z         
2025-03-14T05:14:18.7662065Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7662178Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:14:18.7662306Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:18.7662422Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:18.7662495Z         
2025-03-14T05:14:18.7662944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7663085Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.7663313Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:14:18.7663387Z         
2025-03-14T05:14:18.7663829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7663997Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7664061Z         
2025-03-14T05:14:18.7664435Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7664564Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:18.7664659Z         
2025-03-14T05:14:18.7665089Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.7665228Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:14:18.7665334Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:18.7665460Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:18.7665526Z         
2025-03-14T05:14:18.7665978Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.7666151Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:18.7666386Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:14:18.7666462Z         
2025-03-14T05:14:18.7666910Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.7667082Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.7667147Z         
2025-03-14T05:14:18.7667446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.7667568Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:18.7667665Z         
2025-03-14T05:14:18.7667943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7668323Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:18.7668390Z         
2025-03-14T05:14:18.7668687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7669144Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:18.7669221Z         
2025-03-14T05:14:18.7669492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.7669699Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:14:18.7669766Z         
2025-03-14T05:14:18.7670152Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:18.7670293Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:18.7670374Z         
2025-03-14T05:14:18.7670667Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.7670824Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:14:18.7670907Z         
2025-03-14T05:14:18.7671290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:18.7671446Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:18.7671512Z         
2025-03-14T05:14:18.7671996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:18.7672132Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:14:18.7672261Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:18.7672418Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:18.7672560Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:18.7672624Z         
2025-03-14T05:14:18.7672995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:18.7673114Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:18.7673185Z         
2025-03-14T05:14:18.7673196Z 
2025-03-14T05:14:18.7673297Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:18.7736069Z     def forward(self, L_stack0_tensor: "f32[4, 3, 1184, 1216][4319232, 1439744, 1216, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_: "f32[64, 3, 7, 7][147, 49, 7, 1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_: "f32[64, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_: "f32[64, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_: "f32[64, 64, 3, 3][576, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[64][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_: "f32[256, 64, 1, 1][64, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_: "f32[128, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_: "f32[512, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_: "f32[128, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_: "f32[128, 128, 3, 3][1152, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_: "f32[128][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_: "f32[512, 128, 1, 1][128, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_: "f32[1024, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", 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"f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_: 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L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_: "f32[256][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_: "f32[1024, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_: "f32[1024][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_: "f32[512, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_: "f32[2048, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_: "f32[512, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_: "f32[512, 512, 3, 3][4608, 9, 3, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_: "f32[512][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_: "f32[2048, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_: "f32[2048][1]cpu", L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_: "f32[2048][1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_weight_: "f32[256, 2048, 1, 1][2048, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_0_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_0_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_weight_: "f32[256, 1024, 1, 1][1024, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_1_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_1_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_weight_: "f32[256, 512, 1, 1][512, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_2_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_2_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_weight_: "f32[256, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_backbone_lateral_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_backbone_output_convs_3_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_backbone_output_convs_3_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:14:18.7736530Z         l_stack0_tensor = L_stack0_tensor
2025-03-14T05:14:18.7736873Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7737263Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7737653Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7738026Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7738385Z         l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7738745Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7739159Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7739561Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7739939Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7740313Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7740653Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7741071Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7741478Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7741867Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7742240Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7742589Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7742998Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7743392Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7743774Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7744159Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7744678Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7745226Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7745708Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7746199Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7746670Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7747097Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7747581Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7748069Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7748533Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7748998Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7749423Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7749912Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7750412Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7750874Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7751322Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7751745Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7752237Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7752714Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7753100Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7753507Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7753891Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7754330Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7754769Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7755181Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7755610Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7756003Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7756459Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7756901Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7757316Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7757740Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7758117Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7758579Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7759010Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7759390Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7759772Z         l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7760110Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7760513Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7760899Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7761281Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7761644Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7762030Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7762436Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7762840Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7763222Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7763586Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7763937Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7764333Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7764734Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7765114Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7765495Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7765857Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7766279Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7766693Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7767084Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7767477Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7767828Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7768226Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7768637Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7769026Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7769400Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7769748Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7770199Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7770646Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7771063Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7771663Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7772024Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7772462Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7772886Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7773272Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7773670Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7774016Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7774432Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7774830Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7775244Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7775616Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7775974Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7776379Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7776770Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7777150Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7777518Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7777867Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7778265Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7778670Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7779050Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7779427Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7779771Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7780166Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7780562Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7780940Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7781303Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7781825Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7782226Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7782648Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7783031Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7783418Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7783772Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7784234Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7784674Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7785084Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7785542Z         l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7785892Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7786324Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7786733Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7787114Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7787492Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7787845Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7788257Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7788672Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7789077Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7789456Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7789802Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7790214Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7790611Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7791003Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7791374Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7791764Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7792189Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7792619Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7793025Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7793414Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7793769Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7794173Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7794583Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7794988Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7795360Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7795741Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7796147Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7796555Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7796938Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7797324Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7797679Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7798083Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7798507Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7798894Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7799293Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7799642Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7800059Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7800468Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7800852Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7801232Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7801593Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7802009Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7802431Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7802811Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7803192Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7803537Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7803951Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7804349Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7804768Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7805143Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7805496Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7805891Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7806278Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7806653Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7807022Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7807366Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7807780Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7808169Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7808561Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7808925Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7809268Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7809663Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7810058Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7810437Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7810796Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7811164Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7811559Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7811969Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7812343Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7812717Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7813064Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7813458Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7813854Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7814241Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7814614Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7814969Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7815371Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7815770Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7816144Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7816518Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7816860Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7817265Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7817673Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7818065Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7818438Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7818774Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7819179Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7819569Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7819950Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7820314Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7820686Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7821102Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7821499Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7821879Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7822244Z         l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7822595Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7823010Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7823407Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7823812Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7824249Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7824638Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7825043Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7825453Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7825838Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7826226Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7826589Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7827012Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7827424Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7827830Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7828218Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7828584Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_
2025-03-14T05:14:18.7829010Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7829436Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7829834Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_
2025-03-14T05:14:18.7830235Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_
2025-03-14T05:14:18.7830607Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7831022Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7831447Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7831830Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7832212Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7832562Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7832978Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7833380Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7833793Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7834190Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7834540Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7834957Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7835359Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7835750Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7836125Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7836480Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_
2025-03-14T05:14:18.7836908Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7837305Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7837706Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_
2025-03-14T05:14:18.7838080Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_
2025-03-14T05:14:18.7838437Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_
2025-03-14T05:14:18.7838843Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7839260Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7839653Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_
2025-03-14T05:14:18.7840045Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_
2025-03-14T05:14:18.7840412Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_
2025-03-14T05:14:18.7840823Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_
2025-03-14T05:14:18.7841229Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_
2025-03-14T05:14:18.7841610Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_
2025-03-14T05:14:18.7841990Z         l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = L_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_
2025-03-14T05:14:18.7842237Z         l_self_modules_backbone_lateral_convs_0_parameters_weight_ = L_self_modules_backbone_lateral_convs_0_parameters_weight_
2025-03-14T05:14:18.7842455Z         l_self_modules_backbone_lateral_convs_0_parameters_bias_ = L_self_modules_backbone_lateral_convs_0_parameters_bias_
2025-03-14T05:14:18.7842680Z         l_self_modules_backbone_output_convs_0_parameters_weight_ = L_self_modules_backbone_output_convs_0_parameters_weight_
2025-03-14T05:14:18.7842887Z         l_self_modules_backbone_output_convs_0_parameters_bias_ = L_self_modules_backbone_output_convs_0_parameters_bias_
2025-03-14T05:14:18.7843112Z         l_self_modules_backbone_lateral_convs_1_parameters_weight_ = L_self_modules_backbone_lateral_convs_1_parameters_weight_
2025-03-14T05:14:18.7843342Z         l_self_modules_backbone_lateral_convs_1_parameters_bias_ = L_self_modules_backbone_lateral_convs_1_parameters_bias_
2025-03-14T05:14:18.7843567Z         l_self_modules_backbone_output_convs_1_parameters_weight_ = L_self_modules_backbone_output_convs_1_parameters_weight_
2025-03-14T05:14:18.7843779Z         l_self_modules_backbone_output_convs_1_parameters_bias_ = L_self_modules_backbone_output_convs_1_parameters_bias_
2025-03-14T05:14:18.7844027Z         l_self_modules_backbone_lateral_convs_2_parameters_weight_ = L_self_modules_backbone_lateral_convs_2_parameters_weight_
2025-03-14T05:14:18.7844239Z         l_self_modules_backbone_lateral_convs_2_parameters_bias_ = L_self_modules_backbone_lateral_convs_2_parameters_bias_
2025-03-14T05:14:18.7844465Z         l_self_modules_backbone_output_convs_2_parameters_weight_ = L_self_modules_backbone_output_convs_2_parameters_weight_
2025-03-14T05:14:18.7844681Z         l_self_modules_backbone_output_convs_2_parameters_bias_ = L_self_modules_backbone_output_convs_2_parameters_bias_
2025-03-14T05:14:18.7844901Z         l_self_modules_backbone_lateral_convs_3_parameters_weight_ = L_self_modules_backbone_lateral_convs_3_parameters_weight_
2025-03-14T05:14:18.7845106Z         l_self_modules_backbone_lateral_convs_3_parameters_bias_ = L_self_modules_backbone_lateral_convs_3_parameters_bias_
2025-03-14T05:14:18.7845325Z         l_self_modules_backbone_output_convs_3_parameters_weight_ = L_self_modules_backbone_output_convs_3_parameters_weight_
2025-03-14T05:14:18.7845526Z         l_self_modules_backbone_output_convs_3_parameters_bias_ = L_self_modules_backbone_output_convs_3_parameters_bias_
2025-03-14T05:14:18.7845887Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:14:18.7846259Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:14:18.7846603Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:14:18.7846968Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:14:18.7847313Z         l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:14:18.7847642Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:14:18.7847960Z         l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:14:18.7848326Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:14:18.7848676Z         l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:14:18.7849026Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:14:18.7849366Z         l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:14:18.7849449Z         
2025-03-14T05:14:18.7849726Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7850265Z         x: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.conv2d(l_stack0_tensor, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_, None, (2, 2), (3, 3), (1, 1), 1);  l_stack0_tensor = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7850347Z         
2025-03-14T05:14:18.7850617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7852289Z         x_1: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.nn.functional.batch_norm(x, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_modules_stem_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7852357Z         
2025-03-14T05:14:18.7852645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:357 in forward, code: x = F.relu_(x)
2025-03-14T05:14:18.7853105Z         x_2: "f32[4, 64, 592, 608][23035904, 359936, 608, 1]cpu" = torch.relu_(x_1);  x_1 = None
2025-03-14T05:14:18.7853172Z         
2025-03-14T05:14:18.7853533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:358 in forward, code: x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
2025-03-14T05:14:18.7853784Z         x_3: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.max_pool2d(x_2, kernel_size = 3, stride = 2, padding = 1);  x_2 = None
2025-03-14T05:14:18.7853858Z         
2025-03-14T05:14:18.7854104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7854585Z         x_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7854651Z         
2025-03-14T05:14:18.7854918Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7856659Z         x_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7856756Z         
2025-03-14T05:14:18.7857051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7857206Z         out: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_5);  x_5 = None
2025-03-14T05:14:18.7857280Z         
2025-03-14T05:14:18.7857532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7858032Z         x_6: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7858099Z         
2025-03-14T05:14:18.7858371Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7860155Z         x_7: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_6, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_6 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7860236Z         
2025-03-14T05:14:18.7860532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7860671Z         out_1: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_7);  x_7 = None
2025-03-14T05:14:18.7860748Z         
2025-03-14T05:14:18.7860996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7861500Z         x_8: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_1, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_1 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7861568Z         
2025-03-14T05:14:18.7861840Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7863630Z         x_9: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7863713Z         
2025-03-14T05:14:18.7863984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7864558Z         x_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  x_3 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7864637Z         
2025-03-14T05:14:18.7864912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7866821Z         x_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_10, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_10 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7866920Z         
2025-03-14T05:14:18.7867210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7867358Z         x_9 += x_11;  out_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_9;  x_9 = x_11 = None
2025-03-14T05:14:18.7867432Z         
2025-03-14T05:14:18.7867713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7867872Z         out_3: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_2);  out_2 = None
2025-03-14T05:14:18.7867940Z         
2025-03-14T05:14:18.7868197Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7868684Z         x_12: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_3, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7868757Z         
2025-03-14T05:14:18.7869022Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7870833Z         x_13: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_12, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_12 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7870924Z         
2025-03-14T05:14:18.7871211Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7871365Z         out_4: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_13);  x_13 = None
2025-03-14T05:14:18.7871434Z         
2025-03-14T05:14:18.7871692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7872183Z         x_14: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_4, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_4 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7872259Z         
2025-03-14T05:14:18.7872524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7874308Z         x_15: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_14, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_14 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7874397Z         
2025-03-14T05:14:18.7874676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7874823Z         out_5: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_15);  x_15 = None
2025-03-14T05:14:18.7874887Z         
2025-03-14T05:14:18.7875133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7875633Z         x_16: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_5, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_5 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7875707Z         
2025-03-14T05:14:18.7875976Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7877737Z         x_17: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_16, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_16 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7877828Z         
2025-03-14T05:14:18.7878105Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7878271Z         x_17 += out_3;  out_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_17;  x_17 = out_3 = None
2025-03-14T05:14:18.7878336Z         
2025-03-14T05:14:18.7878619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7878765Z         out_7: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_6);  out_6 = None
2025-03-14T05:14:18.7878839Z         
2025-03-14T05:14:18.7879084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7879582Z         x_18: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_7, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7879656Z         
2025-03-14T05:14:18.7879913Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7881830Z         x_19: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_18, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_18 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7881914Z         
2025-03-14T05:14:18.7882191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7882341Z         out_8: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_19);  x_19 = None
2025-03-14T05:14:18.7882407Z         
2025-03-14T05:14:18.7882656Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7883133Z         x_20: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.conv2d(out_8, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_8 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7883254Z         
2025-03-14T05:14:18.7883510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7885267Z         x_21: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_20, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_20 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7885342Z         
2025-03-14T05:14:18.7885620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7885760Z         out_9: "f32[4, 64, 296, 304][5758976, 89984, 304, 1]cpu" = torch.relu_(x_21);  x_21 = None
2025-03-14T05:14:18.7885823Z         
2025-03-14T05:14:18.7886075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7886588Z         x_22: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_9, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_9 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7886717Z         
2025-03-14T05:14:18.7886975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7888722Z         x_23: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.batch_norm(x_22, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_22 = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_0_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7888798Z         
2025-03-14T05:14:18.7889070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7889228Z         x_23 += out_7;  out_10: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_23;  x_23 = out_7 = None
2025-03-14T05:14:18.7889292Z         
2025-03-14T05:14:18.7889572Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7889738Z         out_11: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.relu_(out_10);  out_10 = None
2025-03-14T05:14:18.7889810Z         
2025-03-14T05:14:18.7890057Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7890553Z         x_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7890619Z         
2025-03-14T05:14:18.7890881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7892620Z         x_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7892689Z         
2025-03-14T05:14:18.7892998Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7893145Z         out_12: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_25);  x_25 = None
2025-03-14T05:14:18.7893240Z         
2025-03-14T05:14:18.7893488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7893984Z         x_26: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_12, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_12 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7894055Z         
2025-03-14T05:14:18.7894316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7896109Z         x_27: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_26, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_26 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7896203Z         
2025-03-14T05:14:18.7896488Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7896641Z         out_13: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_27);  x_27 = None
2025-03-14T05:14:18.7896705Z         
2025-03-14T05:14:18.7896981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7897475Z         x_28: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_13, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_13 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7897551Z         
2025-03-14T05:14:18.7897814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7899610Z         x_29: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_28, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_28 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7899687Z         
2025-03-14T05:14:18.7899940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7900467Z         x_30: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7900532Z         
2025-03-14T05:14:18.7900800Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7902634Z         x_31: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_30, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_30 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7902722Z         
2025-03-14T05:14:18.7903007Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7903158Z         x_29 += x_31;  out_14: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_29;  x_29 = x_31 = None
2025-03-14T05:14:18.7903233Z         
2025-03-14T05:14:18.7903514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7903690Z         out_15: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_14);  out_14 = None
2025-03-14T05:14:18.7903755Z         
2025-03-14T05:14:18.7904012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7904545Z         x_32: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_15, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7904624Z         
2025-03-14T05:14:18.7904889Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7906766Z         x_33: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_32, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_32 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7906859Z         
2025-03-14T05:14:18.7907150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7907302Z         out_16: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_33);  x_33 = None
2025-03-14T05:14:18.7907368Z         
2025-03-14T05:14:18.7907626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7908132Z         x_34: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_16, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_16 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7908199Z         
2025-03-14T05:14:18.7908469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7910239Z         x_35: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_34, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_34 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7910330Z         
2025-03-14T05:14:18.7910632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7910775Z         out_17: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_35);  x_35 = None
2025-03-14T05:14:18.7910849Z         
2025-03-14T05:14:18.7911097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7911596Z         x_36: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_17, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_17 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7911662Z         
2025-03-14T05:14:18.7911932Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7913725Z         x_37: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_36, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_36 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7913814Z         
2025-03-14T05:14:18.7914102Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7914259Z         x_37 += out_15;  out_18: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_37;  x_37 = out_15 = None
2025-03-14T05:14:18.7914334Z         
2025-03-14T05:14:18.7914618Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7914775Z         out_19: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_18);  out_18 = None
2025-03-14T05:14:18.7914841Z         
2025-03-14T05:14:18.7915099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7915586Z         x_38: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_19, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7915658Z         
2025-03-14T05:14:18.7915923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7917743Z         x_39: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_38, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_38 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7917820Z         
2025-03-14T05:14:18.7918096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7918243Z         out_20: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_39);  x_39 = None
2025-03-14T05:14:18.7918306Z         
2025-03-14T05:14:18.7918555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7919028Z         x_40: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_20, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_20 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7919101Z         
2025-03-14T05:14:18.7919377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7921106Z         x_41: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_40, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_40 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7921194Z         
2025-03-14T05:14:18.7921472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7921616Z         out_21: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_41);  x_41 = None
2025-03-14T05:14:18.7921681Z         
2025-03-14T05:14:18.7921931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7922411Z         x_42: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_21, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_21 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7922495Z         
2025-03-14T05:14:18.7922762Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7924514Z         x_43: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_42, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_42 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7924589Z         
2025-03-14T05:14:18.7924868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7925018Z         x_43 += out_19;  out_22: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_43;  x_43 = out_19 = None
2025-03-14T05:14:18.7925088Z         
2025-03-14T05:14:18.7925365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7925518Z         out_23: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_22);  out_22 = None
2025-03-14T05:14:18.7925584Z         
2025-03-14T05:14:18.7925849Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7926317Z         x_44: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_23, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7926402Z         
2025-03-14T05:14:18.7926659Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7928395Z         x_45: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_44, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_44 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7928468Z         
2025-03-14T05:14:18.7928743Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7928887Z         out_24: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_45);  x_45 = None
2025-03-14T05:14:18.7928968Z         
2025-03-14T05:14:18.7929220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7929697Z         x_46: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.conv2d(out_24, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_24 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7929786Z         
2025-03-14T05:14:18.7930046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7931772Z         x_47: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_46, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_46 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7931847Z         
2025-03-14T05:14:18.7932126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7932289Z         out_25: "f32[4, 128, 148, 152][2879488, 22496, 152, 1]cpu" = torch.relu_(x_47);  x_47 = None
2025-03-14T05:14:18.7932353Z         
2025-03-14T05:14:18.7932603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7933100Z         x_48: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.conv2d(out_25, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_25 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7933170Z         
2025-03-14T05:14:18.7933425Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7935149Z         x_49: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.nn.functional.batch_norm(x_48, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_48 = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_1_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7935224Z         
2025-03-14T05:14:18.7935513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7935672Z         x_49 += out_23;  out_26: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = x_49;  x_49 = out_23 = None
2025-03-14T05:14:18.7935736Z         
2025-03-14T05:14:18.7936016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7936186Z         out_27: "f32[4, 512, 148, 152][11517952, 22496, 152, 1]cpu" = torch.relu_(out_26);  out_26 = None
2025-03-14T05:14:18.7936251Z         
2025-03-14T05:14:18.7936489Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7936968Z         x_50: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7937042Z         
2025-03-14T05:14:18.7937302Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7939045Z         x_51: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_50, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_50 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7939132Z         
2025-03-14T05:14:18.7939409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7939551Z         out_28: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_51);  x_51 = None
2025-03-14T05:14:18.7939615Z         
2025-03-14T05:14:18.7939861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7940330Z         x_52: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_28, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_28 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7940403Z         
2025-03-14T05:14:18.7940660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7942401Z         x_53: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_52, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_52 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7942490Z         
2025-03-14T05:14:18.7942787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7942929Z         out_29: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_53);  x_53 = None
2025-03-14T05:14:18.7942992Z         
2025-03-14T05:14:18.7943241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7943710Z         x_54: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_29, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_29 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7943782Z         
2025-03-14T05:14:18.7944040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7945976Z         x_55: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_54, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_54 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7946077Z         
2025-03-14T05:14:18.7946333Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7946832Z         x_56: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.7946899Z         
2025-03-14T05:14:18.7947174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7949008Z         x_57: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_56, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_56 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7949097Z         
2025-03-14T05:14:18.7949391Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7949546Z         x_55 += x_57;  out_30: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_55;  x_55 = x_57 = None
2025-03-14T05:14:18.7949621Z         
2025-03-14T05:14:18.7949902Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7950055Z         out_31: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_30);  out_30 = None
2025-03-14T05:14:18.7950122Z         
2025-03-14T05:14:18.7950377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7950863Z         x_58: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_31, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7950929Z         
2025-03-14T05:14:18.7951199Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7952983Z         x_59: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_58, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_58 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7953072Z         
2025-03-14T05:14:18.7953366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7953503Z         out_32: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_59);  x_59 = None
2025-03-14T05:14:18.7953578Z         
2025-03-14T05:14:18.7953825Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7954319Z         x_60: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_32, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_32 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7954382Z         
2025-03-14T05:14:18.7954652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7956431Z         x_61: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_60, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_60 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7956522Z         
2025-03-14T05:14:18.7956813Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7956950Z         out_33: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_61);  x_61 = None
2025-03-14T05:14:18.7957022Z         
2025-03-14T05:14:18.7957272Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7957762Z         x_62: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_33, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_33 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7957826Z         
2025-03-14T05:14:18.7958093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7959871Z         x_63: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_62, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_62 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7959952Z         
2025-03-14T05:14:18.7960242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7960388Z         x_63 += out_31;  out_34: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_63;  x_63 = out_31 = None
2025-03-14T05:14:18.7960464Z         
2025-03-14T05:14:18.7960752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7960902Z         out_35: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_34);  out_34 = None
2025-03-14T05:14:18.7960965Z         
2025-03-14T05:14:18.7961216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7961680Z         x_64: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_35, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7961767Z         
2025-03-14T05:14:18.7962023Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7963759Z         x_65: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_64, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_64 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7963836Z         
2025-03-14T05:14:18.7964114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7964255Z         out_36: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_65);  x_65 = None
2025-03-14T05:14:18.7964318Z         
2025-03-14T05:14:18.7964571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7965063Z         x_66: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_36, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_36 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7965130Z         
2025-03-14T05:14:18.7965413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7967120Z         x_67: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_66, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_66 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7967194Z         
2025-03-14T05:14:18.7967479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7967607Z         out_37: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_67);  x_67 = None
2025-03-14T05:14:18.7967678Z         
2025-03-14T05:14:18.7967919Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7968428Z         x_68: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_37, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_37 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7968504Z         
2025-03-14T05:14:18.7968768Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7970492Z         x_69: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_68, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_68 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7970569Z         
2025-03-14T05:14:18.7970848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7970991Z         x_69 += out_35;  out_38: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_69;  x_69 = out_35 = None
2025-03-14T05:14:18.7971062Z         
2025-03-14T05:14:18.7971349Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7971503Z         out_39: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_38);  out_38 = None
2025-03-14T05:14:18.7971566Z         
2025-03-14T05:14:18.7971832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7972299Z         x_70: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_39, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7972371Z         
2025-03-14T05:14:18.7972630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7974362Z         x_71: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_70, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_70 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7974454Z         
2025-03-14T05:14:18.7974735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7974874Z         out_40: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_71);  x_71 = None
2025-03-14T05:14:18.7974939Z         
2025-03-14T05:14:18.7975193Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7975682Z         x_72: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_40, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_40 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7975753Z         
2025-03-14T05:14:18.7976012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7977755Z         x_73: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_72, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_72 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7977849Z         
2025-03-14T05:14:18.7978126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7978278Z         out_41: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_73);  x_73 = None
2025-03-14T05:14:18.7978342Z         
2025-03-14T05:14:18.7978594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7979075Z         x_74: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_41, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_41 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7979137Z         
2025-03-14T05:14:18.7979406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7981134Z         x_75: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_74, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_74 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_3_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7981227Z         
2025-03-14T05:14:18.7981668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7981821Z         x_75 += out_39;  out_42: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_75;  x_75 = out_39 = None
2025-03-14T05:14:18.7981895Z         
2025-03-14T05:14:18.7982215Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7982362Z         out_43: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_42);  out_42 = None
2025-03-14T05:14:18.7982426Z         
2025-03-14T05:14:18.7982677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7983145Z         x_76: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_43, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7983217Z         
2025-03-14T05:14:18.7983472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7985320Z         x_77: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_76, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_76 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7985422Z         
2025-03-14T05:14:18.7985710Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7985853Z         out_44: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_77);  x_77 = None
2025-03-14T05:14:18.7985920Z         
2025-03-14T05:14:18.7986196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7986696Z         x_78: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_44, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_44 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7986776Z         
2025-03-14T05:14:18.7987053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7988894Z         x_79: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_78, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_78 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7988995Z         
2025-03-14T05:14:18.7989291Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7989437Z         out_45: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_79);  x_79 = None
2025-03-14T05:14:18.7989507Z         
2025-03-14T05:14:18.7989772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7990265Z         x_80: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_45, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_45 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.7990345Z         
2025-03-14T05:14:18.7990619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7992481Z         x_81: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_80, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_80 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_4_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7992583Z         
2025-03-14T05:14:18.7992868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.7993033Z         x_81 += out_43;  out_46: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_81;  x_81 = out_43 = None
2025-03-14T05:14:18.7993104Z         
2025-03-14T05:14:18.7993400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7993552Z         out_47: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_46);  out_46 = None
2025-03-14T05:14:18.7993627Z         
2025-03-14T05:14:18.7993884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7994378Z         x_82: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_47, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.7994468Z         
2025-03-14T05:14:18.7994727Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7996472Z         x_83: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_82, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_82 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7996548Z         
2025-03-14T05:14:18.7996829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.7996970Z         out_48: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_83);  x_83 = None
2025-03-14T05:14:18.7997035Z         
2025-03-14T05:14:18.7997300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.7997767Z         x_84: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_48, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_48 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.7997853Z         
2025-03-14T05:14:18.7998110Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.7999843Z         x_85: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_84, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_84 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.7999917Z         
2025-03-14T05:14:18.8000194Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8000334Z         out_49: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.relu_(x_85);  x_85 = None
2025-03-14T05:14:18.8000398Z         
2025-03-14T05:14:18.8000647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8001120Z         x_86: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.conv2d(out_49, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_49 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.8001209Z         
2025-03-14T05:14:18.8001467Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8003207Z         x_87: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.nn.functional.batch_norm(x_86, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_86 = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_2_modules_5_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8003282Z         
2025-03-14T05:14:18.8003553Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.8003703Z         x_87 += out_47;  out_50: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = x_87;  x_87 = out_47 = None
2025-03-14T05:14:18.8003766Z         
2025-03-14T05:14:18.8004046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8004185Z         out_51: "f32[4, 1024, 74, 76][5758976, 5624, 76, 1]cpu" = torch.relu_(out_50);  out_50 = None
2025-03-14T05:14:18.8004275Z         
2025-03-14T05:14:18.8004518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8004999Z         x_88: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.8005064Z         
2025-03-14T05:14:18.8005329Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8007028Z         x_89: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_88, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_88 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8007096Z         
2025-03-14T05:14:18.8007377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8007526Z         out_52: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_89);  x_89 = None
2025-03-14T05:14:18.8007598Z         
2025-03-14T05:14:18.8007842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8008334Z         x_90: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_52, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_52 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.8008421Z         
2025-03-14T05:14:18.8008677Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8010393Z         x_91: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_90, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_90 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8010467Z         
2025-03-14T05:14:18.8010755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8010892Z         out_53: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_91);  x_91 = None
2025-03-14T05:14:18.8010955Z         
2025-03-14T05:14:18.8011216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8011687Z         x_92: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_53, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_53 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.8011760Z         
2025-03-14T05:14:18.8012009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8013794Z         x_93: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_92, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_92 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8013885Z         
2025-03-14T05:14:18.8014138Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8014638Z         x_94: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_, None, (2, 2), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_parameters_weight_ = None
2025-03-14T05:14:18.8014704Z         
2025-03-14T05:14:18.8014987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8016819Z         x_95: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_94, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_94 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_0_modules_shortcut_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8016888Z         
2025-03-14T05:14:18.8017174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.8017336Z         x_93 += x_95;  out_54: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_93;  x_93 = x_95 = None
2025-03-14T05:14:18.8017409Z         
2025-03-14T05:14:18.8017688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8017851Z         out_55: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_54);  out_54 = None
2025-03-14T05:14:18.8017916Z         
2025-03-14T05:14:18.8018174Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8018643Z         x_96: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_55, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.8018719Z         
2025-03-14T05:14:18.8018981Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8020743Z         x_97: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_96, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_96 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8020835Z         
2025-03-14T05:14:18.8021120Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8021263Z         out_56: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_97);  x_97 = None
2025-03-14T05:14:18.8021330Z         
2025-03-14T05:14:18.8021601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8022082Z         x_98: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_56, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_56 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.8022156Z         
2025-03-14T05:14:18.8022424Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8024244Z         x_99: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_98, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_98 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8024341Z         
2025-03-14T05:14:18.8024639Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8024784Z         out_57: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_99);  x_99 = None
2025-03-14T05:14:18.8024863Z         
2025-03-14T05:14:18.8025128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8025653Z         x_100: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_57, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_57 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.8025725Z         
2025-03-14T05:14:18.8026016Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8027861Z         x_101: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_100, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_100 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_1_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8027960Z         
2025-03-14T05:14:18.8028248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.8028418Z         x_101 += out_55;  out_58: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_101;  x_101 = out_55 = None
2025-03-14T05:14:18.8028493Z         
2025-03-14T05:14:18.8028779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8028936Z         out_59: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_58);  out_58 = None
2025-03-14T05:14:18.8029007Z         
2025-03-14T05:14:18.8029274Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8029762Z         x_102: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_59, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_parameters_weight_ = None
2025-03-14T05:14:18.8029836Z         
2025-03-14T05:14:18.8030096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8031872Z         x_103: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_102, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_102 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv1_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8031962Z         
2025-03-14T05:14:18.8032248Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:196 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8032395Z         out_60: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_103);  x_103 = None
2025-03-14T05:14:18.8032461Z         
2025-03-14T05:14:18.8032713Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8033192Z         x_104: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.conv2d(out_60, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_, None, (1, 1), (1, 1), (1, 1), 1);  out_60 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_parameters_weight_ = None
2025-03-14T05:14:18.8033265Z         
2025-03-14T05:14:18.8033527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8035309Z         x_105: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_104, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_104 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv2_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8035401Z         
2025-03-14T05:14:18.8035687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:199 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8035833Z         out_61: "f32[4, 512, 37, 38][719872, 1406, 38, 1]cpu" = torch.relu_(x_105);  x_105 = None
2025-03-14T05:14:18.8035899Z         
2025-03-14T05:14:18.8036158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8036644Z         x_106: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.conv2d(out_61, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_, None, (1, 1), (0, 0), (1, 1), 1);  out_61 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_parameters_weight_ = None
2025-03-14T05:14:18.8036719Z         
2025-03-14T05:14:18.8036988Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/batch_norm.py:58 in forward, code: return F.batch_norm(
2025-03-14T05:14:18.8038775Z         x_107: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.nn.functional.batch_norm(x_106, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_, l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_, training = False, eps = 1e-05);  x_106 = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_mean_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_running_var_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_weight_ = l_self_modules_backbone_modules_bottom_up_stages_3_modules_2_modules_conv3_modules_norm_buffers_bias_ = None
2025-03-14T05:14:18.8038865Z         
2025-03-14T05:14:18.8039148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:208 in forward, code: out += shortcut
2025-03-14T05:14:18.8039312Z         x_107 += out_59;  out_62: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = x_107;  x_107 = out_59 = None
2025-03-14T05:14:18.8039393Z         
2025-03-14T05:14:18.8039669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/resnet.py:209 in forward, code: out = F.relu_(out)
2025-03-14T05:14:18.8039818Z         out_63: "f32[4, 2048, 37, 38][2879488, 1406, 38, 1]cpu" = torch.relu_(out_62);  out_62 = None
2025-03-14T05:14:18.8039882Z         
2025-03-14T05:14:18.8040134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8040686Z         x_108: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(out_63, l_self_modules_backbone_lateral_convs_0_parameters_weight_, l_self_modules_backbone_lateral_convs_0_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_63 = l_self_modules_backbone_lateral_convs_0_parameters_weight_ = l_self_modules_backbone_lateral_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.8040778Z         
2025-03-14T05:14:18.8041024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8041582Z         x_109: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_108, l_self_modules_backbone_output_convs_0_parameters_weight_, l_self_modules_backbone_output_convs_0_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_0_parameters_weight_ = l_self_modules_backbone_output_convs_0_parameters_bias_ = None
2025-03-14T05:14:18.8041646Z         
2025-03-14T05:14:18.8042053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.8042318Z         top_down_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.interpolate(x_108, scale_factor = 2.0, mode = 'nearest');  x_108 = None
2025-03-14T05:14:18.8042392Z         
2025-03-14T05:14:18.8042636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8043189Z         x_110: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(out_51, l_self_modules_backbone_lateral_convs_1_parameters_weight_, l_self_modules_backbone_lateral_convs_1_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_51 = l_self_modules_backbone_lateral_convs_1_parameters_weight_ = l_self_modules_backbone_lateral_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.8043260Z         
2025-03-14T05:14:18.8043610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.8043806Z         prev_features: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = x_110 + top_down_features;  x_110 = top_down_features = None
2025-03-14T05:14:18.8043883Z         
2025-03-14T05:14:18.8044132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8044689Z         x_111: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(prev_features, l_self_modules_backbone_output_convs_1_parameters_weight_, l_self_modules_backbone_output_convs_1_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_1_parameters_weight_ = l_self_modules_backbone_output_convs_1_parameters_bias_ = None
2025-03-14T05:14:18.8044761Z         
2025-03-14T05:14:18.8045153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.8045476Z         top_down_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.interpolate(prev_features, scale_factor = 2.0, mode = 'nearest');  prev_features = None
2025-03-14T05:14:18.8045542Z         
2025-03-14T05:14:18.8045793Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8046355Z         x_112: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(out_27, l_self_modules_backbone_lateral_convs_2_parameters_weight_, l_self_modules_backbone_lateral_convs_2_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_27 = l_self_modules_backbone_lateral_convs_2_parameters_weight_ = l_self_modules_backbone_lateral_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.8046426Z         
2025-03-14T05:14:18.8046782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.8046988Z         prev_features_1: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = x_112 + top_down_features_1;  x_112 = top_down_features_1 = None
2025-03-14T05:14:18.8047059Z         
2025-03-14T05:14:18.8047301Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8047884Z         x_113: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(prev_features_1, l_self_modules_backbone_output_convs_2_parameters_weight_, l_self_modules_backbone_output_convs_2_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_self_modules_backbone_output_convs_2_parameters_weight_ = l_self_modules_backbone_output_convs_2_parameters_bias_ = None
2025-03-14T05:14:18.8047948Z         
2025-03-14T05:14:18.8048342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:153 in forward, code: top_down_features = F.interpolate(prev_features, scale_factor=2.0, mode="nearest")
2025-03-14T05:14:18.8048656Z         top_down_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.interpolate(prev_features_1, scale_factor = 2.0, mode = 'nearest');  prev_features_1 = None
2025-03-14T05:14:18.8048731Z         
2025-03-14T05:14:18.8048970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8049536Z         x_114: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(out_11, l_self_modules_backbone_lateral_convs_3_parameters_weight_, l_self_modules_backbone_lateral_convs_3_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  out_11 = l_self_modules_backbone_lateral_convs_3_parameters_weight_ = l_self_modules_backbone_lateral_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.8049601Z         
2025-03-14T05:14:18.8049958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:155 in forward, code: prev_features = lateral_features + top_down_features
2025-03-14T05:14:18.8050172Z         prev_features_2: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = x_114 + top_down_features_2;  x_114 = top_down_features_2 = None
2025-03-14T05:14:18.8050252Z         
2025-03-14T05:14:18.8050508Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8051108Z         x_115: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(prev_features_2, l_self_modules_backbone_output_convs_3_parameters_weight_, l_self_modules_backbone_output_convs_3_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  prev_features_2 = l_self_modules_backbone_output_convs_3_parameters_weight_ = l_self_modules_backbone_output_convs_3_parameters_bias_ = None
2025-03-14T05:14:18.8051181Z         
2025-03-14T05:14:18.8051539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/backbone/fpn.py:200 in forward, code: return [F.max_pool2d(x, kernel_size=1, stride=2, padding=0)]
2025-03-14T05:14:18.8051756Z         res: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.max_pool2d(x_109, kernel_size = 1, stride = 2, padding = 0)
2025-03-14T05:14:18.8051821Z         
2025-03-14T05:14:18.8052260Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8052414Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.8052488Z         
2025-03-14T05:14:18.8052777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8052938Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:18.8053003Z         
2025-03-14T05:14:18.8053428Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8053577Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:14:18.8053651Z         
2025-03-14T05:14:18.8053950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8054098Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:14:18.8054161Z         
2025-03-14T05:14:18.8054532Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.8054707Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:14:18.8054815Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:14:18.8054934Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:14:18.8055005Z         
2025-03-14T05:14:18.8055327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.8055461Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:14:18.8055526Z         
2025-03-14T05:14:18.8055853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.8055996Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:14:18.8056061Z         
2025-03-14T05:14:18.8056436Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.8056663Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:14:18.8056731Z         
2025-03-14T05:14:18.8057134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.8057265Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:14:18.8057676Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:14:18.8057809Z         add_3: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:14:18.8057928Z         x_116: "f32[269952, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:14:18.8057999Z         
2025-03-14T05:14:18.8058417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8058574Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.8058639Z         
2025-03-14T05:14:18.8058938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8059098Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:14:18.8059172Z         
2025-03-14T05:14:18.8059601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8059758Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:14:18.8059824Z         
2025-03-14T05:14:18.8060133Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8060270Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:14:18.8060343Z         
2025-03-14T05:14:18.8060715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.8060915Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:14:18.8061017Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:14:18.8061147Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:14:18.8061211Z         
2025-03-14T05:14:18.8061537Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.8061666Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:14:18.8061739Z         
2025-03-14T05:14:18.8062058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.8062207Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:14:18.8062282Z         
2025-03-14T05:14:18.8062658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.8062895Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:14:18.8062962Z         
2025-03-14T05:14:18.8063379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.8063508Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:14:18.8063931Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:14:18.8064059Z         add_4: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:14:18.8064278Z         x_117: "f32[67488, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:14:18.8064953Z         
2025-03-14T05:14:18.8066210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8066380Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.8066457Z         
2025-03-14T05:14:18.8066751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8066948Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:14:18.8067015Z         
2025-03-14T05:14:18.8067458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8067618Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:14:18.8067696Z         
2025-03-14T05:14:18.8067994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8068144Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:14:18.8068214Z         
2025-03-14T05:14:18.8068603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.8068802Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:14:18.8068918Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:14:18.8069043Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:14:18.8069119Z         
2025-03-14T05:14:18.8069460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.8069595Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:14:18.8069662Z         
2025-03-14T05:14:18.8070008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.8070142Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:14:18.8070209Z         
2025-03-14T05:14:18.8070610Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.8070828Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:14:18.8070903Z         
2025-03-14T05:14:18.8071316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.8071453Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:14:18.8071883Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:14:18.8072013Z         add_5: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:14:18.8072128Z         x_118: "f32[16872, 4][4, 1]cpu" = add_5.reshape(-1, 4);  add_5 = None
2025-03-14T05:14:18.8072201Z         
2025-03-14T05:14:18.8072625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8072790Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.8072872Z         
2025-03-14T05:14:18.8073168Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8073302Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:14:18.8073377Z         
2025-03-14T05:14:18.8073803Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8073967Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:14:18.8074033Z         
2025-03-14T05:14:18.8074331Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8074464Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:14:18.8074540Z         
2025-03-14T05:14:18.8074906Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.8075108Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:14:18.8075210Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:14:18.8075340Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:14:18.8075408Z         
2025-03-14T05:14:18.8075745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.8075878Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:14:18.8075943Z         
2025-03-14T05:14:18.8076279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.8076410Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:14:18.8076502Z         
2025-03-14T05:14:18.8076882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.8077096Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:14:18.8077161Z         
2025-03-14T05:14:18.8077576Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.8077705Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:14:18.8078123Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:14:18.8078248Z         add_6: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:14:18.8078370Z         x_119: "f32[4218, 4][4, 1]cpu" = add_6.reshape(-1, 4);  add_6 = None
2025-03-14T05:14:18.8078437Z         
2025-03-14T05:14:18.8078866Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8079008Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.8079099Z         
2025-03-14T05:14:18.8079385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8079528Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:14:18.8079595Z         
2025-03-14T05:14:18.8080040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:18.8080182Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:18.8080257Z         
2025-03-14T05:14:18.8080547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8080691Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:14:18.8080758Z         
2025-03-14T05:14:18.8081130Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:18.8081319Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:14:18.8081577Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:14:18.8081709Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:14:18.8081783Z         
2025-03-14T05:14:18.8082103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:18.8082236Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:14:18.8082303Z         
2025-03-14T05:14:18.8082668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:18.8082802Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:14:18.8082889Z         
2025-03-14T05:14:18.8083269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:18.8083477Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:14:18.8083552Z         
2025-03-14T05:14:18.8083956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:18.8084091Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:14:18.8084502Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_proposal_generator_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:14:18.8084632Z         add_7: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:14:18.8084750Z         x_120: "f32[1083, 4][4, 1]cpu" = add_7.reshape(-1, 4);  add_7 = None
2025-03-14T05:14:18.8084824Z         
2025-03-14T05:14:18.8085119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.8085255Z         tensor: "f32[269952, 4][4, 1]cpu" = x_116.to(torch.float32);  x_116 = None
2025-03-14T05:14:18.8085416Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_117.to(torch.float32);  x_117 = None
2025-03-14T05:14:18.8085545Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_118.to(torch.float32);  x_118 = None
2025-03-14T05:14:18.8085669Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_119.to(torch.float32);  x_119 = None
2025-03-14T05:14:18.8085796Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_120.to(torch.float32);  x_120 = None
2025-03-14T05:14:18.8085863Z         
2025-03-14T05:14:18.8086148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8086653Z         x_121: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(x_115, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_115 = None
2025-03-14T05:14:18.8086731Z         
2025-03-14T05:14:18.8087008Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.8087215Z         x_122: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_121, inplace = False);  x_121 = None
2025-03-14T05:14:18.8087281Z         
2025-03-14T05:14:18.8087666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.8088192Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.8088258Z         
2025-03-14T05:14:18.8088632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.8089148Z         x_131: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_122, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_122 = None
2025-03-14T05:14:18.8089246Z         
2025-03-14T05:14:18.8089505Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8089991Z         x_123: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(x_113, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_113 = None
2025-03-14T05:14:18.8090058Z         
2025-03-14T05:14:18.8090342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.8090536Z         x_124: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_123, inplace = False);  x_123 = None
2025-03-14T05:14:18.8090609Z         
2025-03-14T05:14:18.8090984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.8091504Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.8091576Z         
2025-03-14T05:14:18.8091946Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.8092464Z         x_132: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_124, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_124 = None
2025-03-14T05:14:18.8092530Z         
2025-03-14T05:14:18.8092831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8093301Z         x_125: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(x_111, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_111 = None
2025-03-14T05:14:18.8093374Z         
2025-03-14T05:14:18.8093647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.8093838Z         x_126: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_125, inplace = False);  x_125 = None
2025-03-14T05:14:18.8093905Z         
2025-03-14T05:14:18.8094285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.8094776Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.8094850Z         
2025-03-14T05:14:18.8095219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.8095720Z         x_133: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_126, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_126 = None
2025-03-14T05:14:18.8095809Z         
2025-03-14T05:14:18.8096058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8096525Z         x_127: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(x_109, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  x_109 = None
2025-03-14T05:14:18.8096591Z         
2025-03-14T05:14:18.8096865Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.8097045Z         x_128: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_127, inplace = False);  x_127 = None
2025-03-14T05:14:18.8097118Z         
2025-03-14T05:14:18.8097482Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.8097976Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:18.8098056Z         
2025-03-14T05:14:18.8098415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.8098910Z         x_134: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_128, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_128 = None
2025-03-14T05:14:18.8098985Z         
2025-03-14T05:14:18.8099254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:18.8100012Z         x_129: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(res, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  res = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:14:18.8100088Z         
2025-03-14T05:14:18.8100365Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:18.8100554Z         x_130: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_129, inplace = False);  x_129 = None
2025-03-14T05:14:18.8100620Z         
2025-03-14T05:14:18.8101006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:18.8101893Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:14:18.8101984Z         
2025-03-14T05:14:18.8102353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:18.8103189Z         x_135: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_130, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_130 = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_proposal_generator_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:14:18.8103268Z         
2025-03-14T05:14:18.8103619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:14:18.8103802Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:14:18.8103956Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:14:18.8104138Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:14:18.8104349Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:14:18.8104523Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:14:18.8104692Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:14:18.8106132Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:14:18.8106374Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:14:18.8106556Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:14:18.8106694Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:14:18.8106775Z         
2025-03-14T05:14:18.8107524Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:14:18.8107721Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_131.view(4, -1, 4, 296, 304);  x_131 = None
2025-03-14T05:14:18.8107925Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:14:18.8108110Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:14:18.8108290Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_132.view(4, -1, 4, 148, 152);  x_132 = None
2025-03-14T05:14:18.8108469Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:14:18.8108654Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:14:18.8108808Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_133.view(4, -1, 4, 74, 76);  x_133 = None
2025-03-14T05:14:18.8108989Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:14:18.8109184Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:14:18.8109343Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_134.view(4, -1, 4, 37, 38);  x_134 = None
2025-03-14T05:14:18.8109538Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:14:18.8109721Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:14:18.8109865Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_135.view(4, -1, 4, 19, 19);  x_135 = None
2025-03-14T05:14:18.8110036Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:14:18.8110206Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:14:18.8110284Z         
2025-03-14T05:14:18.8110706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.8110926Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:14:18.8110993Z         
2025-03-14T05:14:18.8111452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.8111615Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:14:18.8111775Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:18.8111921Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:18.8112022Z         
2025-03-14T05:14:18.8112410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.8112598Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:18.8112671Z         
2025-03-14T05:14:18.8113009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.8113162Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:18.8113230Z         
2025-03-14T05:14:18.8113562Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.8113702Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:18.8113841Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8113997Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:18.8114073Z         
2025-03-14T05:14:18.8114400Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.8114539Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:18.8114664Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:18.8114833Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:14:18.8114900Z         
2025-03-14T05:14:18.8115258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.8115389Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8115489Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:18.8115637Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:14:18.8115713Z         
2025-03-14T05:14:18.8116030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.8116189Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:18.8116284Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:18.8116426Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:14:18.8116495Z         
2025-03-14T05:14:18.8116846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.8117003Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.8117128Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:14:18.8117193Z         
2025-03-14T05:14:18.8117499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.8117653Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.8117777Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:14:18.8117840Z         
2025-03-14T05:14:18.8118140Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.8118311Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.8118431Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:14:18.8118497Z         
2025-03-14T05:14:18.8118805Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.8119003Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:18.8119125Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:14:18.8119190Z         
2025-03-14T05:14:18.8119529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.8119679Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:18.8119744Z         
2025-03-14T05:14:18.8120075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.8120212Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:18.8120283Z         
2025-03-14T05:14:18.8120626Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.8120773Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:18.8120898Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:14:18.8121077Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:18.8121219Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:14:18.8121291Z         
2025-03-14T05:14:18.8121649Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.8121800Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:18.8121926Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:14:18.8122085Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:18.8122223Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:14:18.8122297Z         
2025-03-14T05:14:18.8122627Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.8122754Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:18.8122918Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:18.8123058Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:14:18.8123124Z         
2025-03-14T05:14:18.8123463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.8123580Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:18.8123753Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:18.8123908Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:14:18.8123986Z         
2025-03-14T05:14:18.8124293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.8124403Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:18.8124522Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:18.8124596Z         
2025-03-14T05:14:18.8124917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.8125022Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:18.8125138Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:18.8125214Z         
2025-03-14T05:14:18.8125530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.8125654Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:18.8125786Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:18.8125860Z         
2025-03-14T05:14:18.8126165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.8126286Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:18.8126413Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:18.8126486Z         
2025-03-14T05:14:18.8126832Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.8127037Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:18.8127109Z         
2025-03-14T05:14:18.8127468Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.8127631Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:18.8127705Z         
2025-03-14T05:14:18.8128081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.8128266Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:18.8128340Z         
2025-03-14T05:14:18.8128738Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.8128956Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:18.8129023Z         
2025-03-14T05:14:18.8129461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.8129617Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:14:18.8129778Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:18.8129916Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:18.8130012Z         
2025-03-14T05:14:18.8130382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.8130562Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:18.8130629Z         
2025-03-14T05:14:18.8130963Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.8131113Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:18.8131186Z         
2025-03-14T05:14:18.8131499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.8131645Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:18.8131774Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8131934Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:14:18.8132001Z         
2025-03-14T05:14:18.8132322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.8132449Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:18.8132580Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:18.8132732Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:14:18.8132805Z         
2025-03-14T05:14:18.8133127Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.8133258Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8133353Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:18.8133515Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:14:18.8133579Z         
2025-03-14T05:14:18.8133898Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.8134047Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:18.8134151Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:18.8134281Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:14:18.8134358Z         
2025-03-14T05:14:18.8134665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.8134826Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.8134944Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:14:18.8135014Z         
2025-03-14T05:14:18.8135323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.8135474Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.8135595Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:14:18.8135659Z         
2025-03-14T05:14:18.8135970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.8136141Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.8136262Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:14:18.8136330Z         
2025-03-14T05:14:18.8136634Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.8136842Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:18.8136963Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:14:18.8137028Z         
2025-03-14T05:14:18.8137368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.8137514Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:18.8137588Z         
2025-03-14T05:14:18.8137917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.8138066Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:18.8138131Z         
2025-03-14T05:14:18.8138479Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.8138617Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.8138756Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:14:18.8138933Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:18.8139088Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:14:18.8139170Z         
2025-03-14T05:14:18.8139523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.8139662Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:18.8139795Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:14:18.8139950Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:18.8140099Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:14:18.8140165Z         
2025-03-14T05:14:18.8140501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.8140619Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:18.8140788Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:18.8140933Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:14:18.8141001Z         
2025-03-14T05:14:18.8141335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.8141472Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:18.8141645Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:18.8141800Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:14:18.8141872Z         
2025-03-14T05:14:18.8142185Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.8142294Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:18.8142446Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:18.8142521Z         
2025-03-14T05:14:18.8142823Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.8142927Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:18.8143044Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:18.8143118Z         
2025-03-14T05:14:18.8143417Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.8143541Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:18.8143674Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:18.8143746Z         
2025-03-14T05:14:18.8144049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.8144244Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:18.8144391Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:18.8144467Z         
2025-03-14T05:14:18.8144844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.8145067Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:18.8145153Z         
2025-03-14T05:14:18.8145495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.8145672Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:18.8145749Z         
2025-03-14T05:14:18.8146144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.8146340Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:18.8146409Z         
2025-03-14T05:14:18.8146829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.8147044Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:18.8147122Z         
2025-03-14T05:14:18.8147565Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.8147730Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:14:18.8147889Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:18.8148058Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:18.8148125Z         
2025-03-14T05:14:18.8148507Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.8148686Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:14:18.8148754Z         
2025-03-14T05:14:18.8149093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.8149241Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:18.8149314Z         
2025-03-14T05:14:18.8149635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.8149777Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:18.8149904Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8150064Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:14:18.8150131Z         
2025-03-14T05:14:18.8150462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.8150588Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:18.8150717Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:18.8150872Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:14:18.8150948Z         
2025-03-14T05:14:18.8151279Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.8151432Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8151527Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:18.8151670Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:14:18.8151738Z         
2025-03-14T05:14:18.8152066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.8152218Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:18.8152324Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:18.8152460Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:14:18.8152533Z         
2025-03-14T05:14:18.8152846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.8153016Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.8153133Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:14:18.8153209Z         
2025-03-14T05:14:18.8153519Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.8153684Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.8153800Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:14:18.8153893Z         
2025-03-14T05:14:18.8154196Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.8154359Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.8154475Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:14:18.8154555Z         
2025-03-14T05:14:18.8154875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.8155074Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:18.8155195Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:14:18.8155262Z         
2025-03-14T05:14:18.8155612Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.8155754Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:18.8155831Z         
2025-03-14T05:14:18.8156167Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.8156310Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:18.8156375Z         
2025-03-14T05:14:18.8156716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.8156848Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.8156994Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:14:18.8157145Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:18.8157304Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:14:18.8157367Z         
2025-03-14T05:14:18.8157717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.8157851Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:18.8157977Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:14:18.8158124Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:18.8158269Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:14:18.8158331Z         
2025-03-14T05:14:18.8158665Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.8158779Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:18.8158941Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:18.8159075Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:14:18.8159148Z         
2025-03-14T05:14:18.8159470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.8159589Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:18.8159773Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:18.8159913Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:14:18.8159979Z         
2025-03-14T05:14:18.8160283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.8160380Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:18.8160514Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:18.8160579Z         
2025-03-14T05:14:18.8160881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.8160975Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:18.8161097Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:18.8161160Z         
2025-03-14T05:14:18.8161462Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.8161574Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:18.8161712Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:18.8161774Z         
2025-03-14T05:14:18.8162076Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.8162187Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:18.8162326Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:18.8162392Z         
2025-03-14T05:14:18.8162744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.8162953Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:18.8163016Z         
2025-03-14T05:14:18.8163340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.8163499Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:18.8163569Z         
2025-03-14T05:14:18.8163927Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.8164105Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:18.8164168Z         
2025-03-14T05:14:18.8164558Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.8164757Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:14:18.8164829Z         
2025-03-14T05:14:18.8165242Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.8165393Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:14:18.8165555Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:18.8165696Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:18.8165760Z         
2025-03-14T05:14:18.8166129Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.8166308Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:14:18.8166381Z         
2025-03-14T05:14:18.8166684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.8166833Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:18.8166899Z         
2025-03-14T05:14:18.8167213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.8167338Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:18.8167470Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8167614Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:14:18.8167685Z         
2025-03-14T05:14:18.8167994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.8168122Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:18.8168239Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:18.8168410Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:14:18.8168477Z         
2025-03-14T05:14:18.8168784Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.8168918Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8169017Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:18.8169145Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:14:18.8169218Z         
2025-03-14T05:14:18.8169521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.8169673Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:18.8169767Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:18.8169901Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:14:18.8169964Z         
2025-03-14T05:14:18.8170269Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.8170428Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.8170543Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:14:18.8170612Z         
2025-03-14T05:14:18.8170907Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.8171058Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.8171183Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:14:18.8171255Z         
2025-03-14T05:14:18.8171540Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.8171693Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.8171801Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:14:18.8171871Z         
2025-03-14T05:14:18.8172182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.8172369Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:18.8172476Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:14:18.8172549Z         
2025-03-14T05:14:18.8172875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.8173019Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:18.8173083Z         
2025-03-14T05:14:18.8173413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.8173547Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:18.8173620Z         
2025-03-14T05:14:18.8173955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.8174095Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.8174241Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:14:18.8174400Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:18.8174555Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:14:18.8174627Z         
2025-03-14T05:14:18.8174957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.8175097Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:18.8175215Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:14:18.8175369Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:18.8175512Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:14:18.8175575Z         
2025-03-14T05:14:18.8175899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.8176012Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:18.8176173Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:18.8176301Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:14:18.8176371Z         
2025-03-14T05:14:18.8176687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.8176824Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:18.8176985Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:18.8177120Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:14:18.8177186Z         
2025-03-14T05:14:18.8177495Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.8177608Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:18.8177728Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:18.8177790Z         
2025-03-14T05:14:18.8178103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.8178201Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:18.8178323Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:18.8178387Z         
2025-03-14T05:14:18.8178693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.8178809Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:18.8178948Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:18.8179015Z         
2025-03-14T05:14:18.8179323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.8179434Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:18.8179574Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:18.8179657Z         
2025-03-14T05:14:18.8180010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.8180221Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:18.8180295Z         
2025-03-14T05:14:18.8180630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.8180800Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:18.8180866Z         
2025-03-14T05:14:18.8181256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.8181634Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:18.8181717Z         
2025-03-14T05:14:18.8182128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:18.8182349Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:14:18.8182415Z         
2025-03-14T05:14:18.8182862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:18.8183075Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:14:18.8183227Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:18.8183369Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:18.8183437Z         
2025-03-14T05:14:18.8183815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:18.8184013Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:14:18.8184094Z         
2025-03-14T05:14:18.8184486Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:18.8184650Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:18.8184721Z         
2025-03-14T05:14:18.8185063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:18.8185202Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:18.8185349Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8185499Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:14:18.8185575Z         
2025-03-14T05:14:18.8185901Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:18.8186035Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:18.8186160Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:18.8186349Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:14:18.8186418Z         
2025-03-14T05:14:18.8186744Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:18.8186905Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:18.8187003Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:18.8187135Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:14:18.8187207Z         
2025-03-14T05:14:18.8187517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:18.8187670Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:18.8187766Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:18.8187905Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:14:18.8187972Z         
2025-03-14T05:14:18.8188293Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:18.8188447Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:18.8188575Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:14:18.8188643Z         
2025-03-14T05:14:18.8188958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:18.8189111Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:18.8189292Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:14:18.8189359Z         
2025-03-14T05:14:18.8189669Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:18.8189829Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:18.8189946Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:14:18.8190025Z         
2025-03-14T05:14:18.8190332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:18.8190520Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:18.8190630Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:14:18.8190706Z         
2025-03-14T05:14:18.8191046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:18.8191197Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:18.8191264Z         
2025-03-14T05:14:18.8191607Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:18.8191743Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:18.8191815Z         
2025-03-14T05:14:18.8193170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:18.8193382Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.8193509Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:14:18.8193671Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:18.8193829Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:14:18.8193901Z         
2025-03-14T05:14:18.8194267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:18.8194410Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:18.8194529Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:14:18.8194688Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:18.8194822Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:14:18.8194894Z         
2025-03-14T05:14:18.8195219Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:18.8195340Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:18.8195499Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:18.8195641Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:14:18.8195707Z         
2025-03-14T05:14:18.8196040Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:18.8196174Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:18.8196349Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:18.8196480Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:14:18.8196553Z         
2025-03-14T05:14:18.8196879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:18.8196988Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:18.8197102Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:18.8197177Z         
2025-03-14T05:14:18.8197477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:18.8197585Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:18.8197699Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:18.8197773Z         
2025-03-14T05:14:18.8198075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:18.8198198Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:18.8198332Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:18.8198406Z         
2025-03-14T05:14:18.8198702Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:18.8198824Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:18.8198969Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:18.8199042Z         
2025-03-14T05:14:18.8199382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:18.8199591Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:18.8199663Z         
2025-03-14T05:14:18.8199993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:18.8200158Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:18.8200223Z         
2025-03-14T05:14:18.8200603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:18.8200775Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:18.8200860Z         
2025-03-14T05:14:18.8201335Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:18.8201478Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:18.8201543Z         
2025-03-14T05:14:18.8201845Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8201986Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:14:18.8202075Z         
2025-03-14T05:14:18.8202511Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.8202631Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:14:18.8202744Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:18.8202863Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:18.8202927Z         
2025-03-14T05:14:18.8203393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.8203526Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.8203764Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:14:18.8203829Z         
2025-03-14T05:14:18.8224156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.8224455Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.8224546Z         
2025-03-14T05:14:18.8224894Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8225042Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:18.8225118Z         
2025-03-14T05:14:18.8225678Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.8225821Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:14:18.8225983Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:18.8226116Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:18.8226198Z         
2025-03-14T05:14:18.8226701Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.8226842Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.8227097Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:14:18.8227171Z         
2025-03-14T05:14:18.8227653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.8227830Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.8227906Z         
2025-03-14T05:14:18.8228214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8228354Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:18.8228423Z         
2025-03-14T05:14:18.8228908Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.8229057Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:14:18.8229184Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:18.8229319Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:18.8229393Z         
2025-03-14T05:14:18.8229893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.8230043Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.8230291Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:14:18.8230370Z         
2025-03-14T05:14:18.8230843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.8231023Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.8231092Z         
2025-03-14T05:14:18.8231406Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8231537Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:18.8231612Z         
2025-03-14T05:14:18.8232058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.8232201Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:14:18.8232313Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:18.8232439Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:18.8232525Z         
2025-03-14T05:14:18.8233010Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.8233161Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:18.8233410Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:14:18.8233488Z         
2025-03-14T05:14:18.8233966Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.8234149Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.8234222Z         
2025-03-14T05:14:18.8234541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8234675Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:18.8234754Z         
2025-03-14T05:14:18.8235204Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:18.8235332Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:14:18.8235466Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:18.8235595Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:18.8235663Z         
2025-03-14T05:14:18.8236145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:18.8236333Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:18.8236584Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:14:18.8236653Z         
2025-03-14T05:14:18.8237132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:18.8237301Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:18.8237381Z         
2025-03-14T05:14:18.8237684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:18.8237819Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:18.8237889Z         
2025-03-14T05:14:18.8238191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.8238581Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:18.8238674Z         
2025-03-14T05:14:18.8238975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.8239471Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:18.8239547Z         
2025-03-14T05:14:18.8239834Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:18.8240052Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:14:18.8240123Z         
2025-03-14T05:14:18.8240530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:18.8240680Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:18.8240756Z         
2025-03-14T05:14:18.8241066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:18.8241227Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:14:18.8241296Z         
2025-03-14T05:14:18.8241700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:18.8241839Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:18.8241980Z         
2025-03-14T05:14:18.8242484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:18.8242638Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:14:18.8242763Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:18.8242948Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:18.8243088Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:18.8243166Z         
2025-03-14T05:14:18.8243551Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:18.8243685Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:18.8243817Z         
2025-03-14T05:14:25.4430778Z 
2025-03-14T05:14:25.4435755Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:25.4440609Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:14:25.4443267Z         l_features_p2_ = L_features_p2_
2025-03-14T05:14:25.4443513Z         l_features_p3_ = L_features_p3_
2025-03-14T05:14:25.4443750Z         l_features_p4_ = L_features_p4_
2025-03-14T05:14:25.4443979Z         l_features_p5_ = L_features_p5_
2025-03-14T05:14:25.4444207Z         l_features_p6_ = L_features_p6_
2025-03-14T05:14:25.4444622Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:14:25.4445225Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:14:25.4445813Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:14:25.4446408Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:14:25.4446994Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:14:25.4447563Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:14:25.4448088Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:14:25.4448676Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:14:25.4449258Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:14:25.4449817Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:14:25.4450404Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:14:25.4450769Z         
2025-03-14T05:14:25.4451350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4452017Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:14:25.4452290Z         
2025-03-14T05:14:25.4452688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4453185Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:25.4453444Z         
2025-03-14T05:14:25.4453972Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4454604Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:14:25.4454878Z         
2025-03-14T05:14:25.4455262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4455773Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:14:25.4456038Z         
2025-03-14T05:14:25.4456503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4457123Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:14:25.4457461Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:14:25.4457738Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:14:25.4457986Z         
2025-03-14T05:14:25.4458410Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4458926Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:14:25.4459181Z         
2025-03-14T05:14:25.4459599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4460104Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:14:25.4460354Z         
2025-03-14T05:14:25.4460817Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4461460Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:14:25.4461792Z         
2025-03-14T05:14:25.4462296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4462901Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:14:25.4463400Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:14:25.4463891Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:14:25.4464367Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:14:25.4464640Z         
2025-03-14T05:14:25.4465232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4465924Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:14:25.4466222Z         
2025-03-14T05:14:25.4466625Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4467146Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:14:25.4467433Z         
2025-03-14T05:14:25.4467977Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4468643Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:14:25.4468934Z         
2025-03-14T05:14:25.4469336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4469864Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:14:25.4470139Z         
2025-03-14T05:14:25.4470617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4471291Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:14:25.4471672Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:14:25.4471978Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:14:25.4472234Z         
2025-03-14T05:14:25.4472693Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4473254Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:14:25.4473526Z         
2025-03-14T05:14:25.4473980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4474537Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:14:25.4474809Z         
2025-03-14T05:14:25.4475322Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4476029Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:14:25.4476389Z         
2025-03-14T05:14:25.4476938Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4477609Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:14:25.4478137Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:14:25.4478659Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:14:25.4479002Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:14:25.4479243Z         
2025-03-14T05:14:25.4479764Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4480392Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:14:25.4480658Z         
2025-03-14T05:14:25.4481037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4481910Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:14:25.4482194Z         
2025-03-14T05:14:25.4482745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4483408Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:14:25.4483697Z         
2025-03-14T05:14:25.4484091Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4484638Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:14:25.4484910Z         
2025-03-14T05:14:25.4485380Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4486042Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:14:25.4486402Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:14:25.4486684Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:14:25.4486932Z         
2025-03-14T05:14:25.4487357Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4487880Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:14:25.4488136Z         
2025-03-14T05:14:25.4488554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4489062Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:14:25.4489311Z         
2025-03-14T05:14:25.4489781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4490430Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:14:25.4490764Z         
2025-03-14T05:14:25.4491266Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4491903Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:14:25.4492440Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:14:25.4492982Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:14:25.4493358Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:14:25.4493634Z         
2025-03-14T05:14:25.4494179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4494840Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:14:25.4495126Z         
2025-03-14T05:14:25.4495514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4496023Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:14:25.4496294Z         
2025-03-14T05:14:25.4496835Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4497489Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:14:25.4497770Z         
2025-03-14T05:14:25.4498165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4498689Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:14:25.4498961Z         
2025-03-14T05:14:25.4499442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4500107Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:14:25.4500475Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:14:25.4500764Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:14:25.4501013Z         
2025-03-14T05:14:25.4501457Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4501995Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:14:25.4502257Z         
2025-03-14T05:14:25.4502696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4503230Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:14:25.4503490Z         
2025-03-14T05:14:25.4503983Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4507082Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:14:25.4508411Z         
2025-03-14T05:14:25.4509014Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4509707Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:14:25.4510218Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:14:25.4510727Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:14:25.4511074Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:14:25.4511321Z         
2025-03-14T05:14:25.4511856Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4512863Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:25.4513201Z         
2025-03-14T05:14:25.4513603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4514107Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:14:25.4514376Z         
2025-03-14T05:14:25.4515217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4516982Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:25.4518089Z         
2025-03-14T05:14:25.4518539Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4519100Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:14:25.4519968Z         
2025-03-14T05:14:25.4520688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4521999Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:14:25.4522409Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:14:25.4522707Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:14:25.4522960Z         
2025-03-14T05:14:25.4523402Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4524768Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:14:25.4525067Z         
2025-03-14T05:14:25.4525958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4526489Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:14:25.4526740Z         
2025-03-14T05:14:25.4527214Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4527859Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:14:25.4528194Z         
2025-03-14T05:14:25.4529182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4529832Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:14:25.4530332Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:14:25.4530820Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:14:25.4531147Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:14:25.4531387Z         
2025-03-14T05:14:25.4531781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:25.4532449Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:14:25.4532768Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:14:25.4533089Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:14:25.4533383Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:14:25.4534028Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:14:25.4534282Z         
2025-03-14T05:14:25.4534640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4535386Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:14:25.4535932Z         
2025-03-14T05:14:25.4536324Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4536861Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:14:25.4537171Z         
2025-03-14T05:14:25.4537867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4538713Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4539401Z         
2025-03-14T05:14:25.4539870Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4540717Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:14:25.4541260Z         
2025-03-14T05:14:25.4541619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4542361Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:14:25.4542905Z         
2025-03-14T05:14:25.4543281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4543830Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:14:25.4544153Z         
2025-03-14T05:14:25.4544721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4545614Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4546152Z         
2025-03-14T05:14:25.4546617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4547457Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:14:25.4547999Z         
2025-03-14T05:14:25.4548354Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4549086Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:14:25.4549611Z         
2025-03-14T05:14:25.4549987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4550516Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:14:25.4550832Z         
2025-03-14T05:14:25.4551323Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4552174Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4552717Z         
2025-03-14T05:14:25.4553170Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4553982Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:14:25.4554505Z         
2025-03-14T05:14:25.4554855Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4555568Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:14:25.4556076Z         
2025-03-14T05:14:25.4556432Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4556938Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:14:25.4557235Z         
2025-03-14T05:14:25.4557706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4558587Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4559218Z         
2025-03-14T05:14:25.4559745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4560955Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:14:25.4561528Z         
2025-03-14T05:14:25.4561934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4562966Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:14:25.4563746Z         
2025-03-14T05:14:25.4564201Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4564808Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:14:25.4565178Z         
2025-03-14T05:14:25.4578472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4579778Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:14:25.4580595Z         
2025-03-14T05:14:25.4581075Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4582339Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:14:25.4583077Z         
2025-03-14T05:14:25.4583530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:14:25.4584114Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:14:25.4584572Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:14:25.4584963Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:14:25.4585356Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:14:25.4585733Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:14:25.4586228Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:14:25.4586586Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:14:25.4586942Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:14:25.4587301Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:14:25.4587689Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:14:25.4587957Z         
2025-03-14T05:14:25.4588496Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:14:25.4589168Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:14:25.4589600Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:14:25.4590033Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:14:25.4590585Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:14:25.4590993Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:14:25.4591408Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:14:25.4591796Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:14:25.4592181Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:14:25.4592626Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:14:25.4593020Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:14:25.4593443Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:14:25.4593844Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:14:25.4594228Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:14:25.4594592Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:14:25.4594989Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:14:25.4595292Z         
2025-03-14T05:14:25.4595815Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4596505Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:14:25.4596842Z         
2025-03-14T05:14:25.4597384Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4598049Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:14:25.4598422Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:25.4598810Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:25.4599083Z         
2025-03-14T05:14:25.4599567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4600187Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:25.4600489Z         
2025-03-14T05:14:25.4600926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4601459Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:25.4601744Z         
2025-03-14T05:14:25.4602153Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4602669Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4602990Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4603336Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:25.4603614Z         
2025-03-14T05:14:25.4604025Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4604528Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4604838Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4605176Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:14:25.4605457Z         
2025-03-14T05:14:25.4605880Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4606384Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4606681Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:25.4606960Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:14:25.4607215Z         
2025-03-14T05:14:25.4607621Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4608145Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:25.4608450Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:25.4608729Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:14:25.4608990Z         
2025-03-14T05:14:25.4609422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4609946Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4610285Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:14:25.4610524Z         
2025-03-14T05:14:25.4610917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4611431Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4611759Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:14:25.4611996Z         
2025-03-14T05:14:25.4612415Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4612931Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4613259Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:14:25.4613496Z         
2025-03-14T05:14:25.4613888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4614537Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:25.4614894Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:14:25.4615134Z         
2025-03-14T05:14:25.4615566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4616111Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:25.4616379Z         
2025-03-14T05:14:25.4616801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4617330Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:25.4617593Z         
2025-03-14T05:14:25.4618026Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4618565Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:25.4618890Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:14:25.4619257Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:25.4619622Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:14:25.4619911Z         
2025-03-14T05:14:25.4620347Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4620902Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:25.4621232Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:14:25.4621571Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:25.4621924Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:14:25.4622197Z         
2025-03-14T05:14:25.4622629Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4623145Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:25.4623491Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:25.4623854Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:14:25.4624121Z         
2025-03-14T05:14:25.4624646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4625175Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:25.4625538Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:25.4625970Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:14:25.4626253Z         
2025-03-14T05:14:25.4626666Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4627145Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:25.4627427Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:25.4627699Z         
2025-03-14T05:14:25.4628108Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4628584Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:25.4628856Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:25.4629104Z         
2025-03-14T05:14:25.4629513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4630010Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:25.4630324Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:25.4630584Z         
2025-03-14T05:14:25.4630990Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4631485Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:25.4631792Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:25.4632051Z         
2025-03-14T05:14:25.4632514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4633122Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:25.4633441Z         
2025-03-14T05:14:25.4633874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4634438Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:25.4634742Z         
2025-03-14T05:14:25.4635217Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4635830Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:25.4636130Z         
2025-03-14T05:14:25.4636617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4637278Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:25.4637607Z         
2025-03-14T05:14:25.4638118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4638752Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:14:25.4639111Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:25.4639494Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:25.4639756Z         
2025-03-14T05:14:25.4640206Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4640816Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:25.4641107Z         
2025-03-14T05:14:25.4641523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4642035Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:25.4642297Z         
2025-03-14T05:14:25.4642700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4643203Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4643520Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4643861Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:14:25.4644138Z         
2025-03-14T05:14:25.4644546Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4645041Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4645349Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4645683Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:14:25.4645963Z         
2025-03-14T05:14:25.4646385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4646872Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4647173Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:25.4647457Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:14:25.4647712Z         
2025-03-14T05:14:25.4648117Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4648642Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:25.4648942Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:25.4649220Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:14:25.4649474Z         
2025-03-14T05:14:25.4649875Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4650390Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4650718Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:14:25.4650957Z         
2025-03-14T05:14:25.4651346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4651858Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4652183Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:14:25.4652444Z         
2025-03-14T05:14:25.4652827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4653331Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4653661Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:14:25.4653906Z         
2025-03-14T05:14:25.4654320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4654855Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:25.4655206Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:14:25.4655440Z         
2025-03-14T05:14:25.4655862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4656392Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:25.4656653Z         
2025-03-14T05:14:25.4657063Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4657584Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:25.4657838Z         
2025-03-14T05:14:25.4658267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4658801Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:25.4659145Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:14:25.4659490Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:25.4659849Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:14:25.4660135Z         
2025-03-14T05:14:25.4660566Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4661116Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:25.4661440Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:14:25.4661771Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:25.4662122Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:14:25.4662386Z         
2025-03-14T05:14:25.4662810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4663316Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:25.4663654Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:25.4664013Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:14:25.4664356Z         
2025-03-14T05:14:25.4664801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4665319Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:25.4665715Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:25.4666073Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:14:25.4666340Z         
2025-03-14T05:14:25.4666745Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4667220Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:25.4667521Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:25.4667770Z         
2025-03-14T05:14:25.4668171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4668643Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:25.4668920Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:25.4669165Z         
2025-03-14T05:14:25.4669571Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4670065Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:25.4670387Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:25.4670651Z         
2025-03-14T05:14:25.4671051Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4671539Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:25.4671852Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:25.4672115Z         
2025-03-14T05:14:25.4672585Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4673198Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:25.4673545Z         
2025-03-14T05:14:25.4673973Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4674539Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:25.4674834Z         
2025-03-14T05:14:25.4675320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4675945Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:25.4676249Z         
2025-03-14T05:14:25.4676753Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4677432Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:25.4677766Z         
2025-03-14T05:14:25.4678292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4678941Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:14:25.4679333Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:25.4679678Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:25.4679938Z         
2025-03-14T05:14:25.4680394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4681010Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:14:25.4681299Z         
2025-03-14T05:14:25.4681853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4682366Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:25.4682629Z         
2025-03-14T05:14:25.4683032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4683535Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4683851Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4684183Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:14:25.4684456Z         
2025-03-14T05:14:25.4684862Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4685355Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4685655Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4686057Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:14:25.4686329Z         
2025-03-14T05:14:25.4686731Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4687253Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4687529Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:25.4687812Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:14:25.4688069Z         
2025-03-14T05:14:25.4688469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4688993Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:25.4689302Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:25.4689581Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:14:25.4689833Z         
2025-03-14T05:14:25.4690229Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4690743Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4691076Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:14:25.4691318Z         
2025-03-14T05:14:25.4691704Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4692205Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4692568Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:14:25.4692811Z         
2025-03-14T05:14:25.4693200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4693705Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4694030Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:14:25.4694268Z         
2025-03-14T05:14:25.4694685Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4695224Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:25.4695574Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:14:25.4695808Z         
2025-03-14T05:14:25.4696233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4696766Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:25.4697031Z         
2025-03-14T05:14:25.4697446Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4697979Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:25.4698238Z         
2025-03-14T05:14:25.4698670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4699204Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:25.4699550Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:14:25.4699889Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:25.4700266Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:14:25.4700530Z         
2025-03-14T05:14:25.4700958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4701500Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:25.4701814Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:14:25.4702148Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:25.4702500Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:14:25.4702763Z         
2025-03-14T05:14:25.4703184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4703683Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:25.4704025Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:25.4704474Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:14:25.4704746Z         
2025-03-14T05:14:25.4705191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4705746Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:25.4706086Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:25.4706443Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:14:25.4706712Z         
2025-03-14T05:14:25.4707113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4707595Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:25.4707870Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:25.4708114Z         
2025-03-14T05:14:25.4708510Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4708978Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:25.4709247Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:25.4709488Z         
2025-03-14T05:14:25.4709883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4710373Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:25.4710691Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:25.4710952Z         
2025-03-14T05:14:25.4711346Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4711827Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:25.4712131Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:25.4712405Z         
2025-03-14T05:14:25.4712843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4713457Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:25.4713761Z         
2025-03-14T05:14:25.4714181Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4714728Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:25.4715010Z         
2025-03-14T05:14:25.4715471Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4716078Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:25.4716368Z         
2025-03-14T05:14:25.4716850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4717499Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:14:25.4717820Z         
2025-03-14T05:14:25.4718368Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4718999Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:14:25.4719380Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:25.4719725Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:25.4719991Z         
2025-03-14T05:14:25.4720448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4721056Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:14:25.4721348Z         
2025-03-14T05:14:25.4721737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4722258Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:25.4722519Z         
2025-03-14T05:14:25.4722917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4723418Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4723730Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4724064Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:14:25.4724337Z         
2025-03-14T05:14:25.4724739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4725233Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4725535Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4725899Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:14:25.4726174Z         
2025-03-14T05:14:25.4726579Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4727090Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4727371Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:25.4727657Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:14:25.4727913Z         
2025-03-14T05:14:25.4728312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4728826Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:25.4729130Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:25.4729407Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:14:25.4729661Z         
2025-03-14T05:14:25.4730059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4730574Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4730904Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:14:25.4731144Z         
2025-03-14T05:14:25.4731531Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4732032Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4732376Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:14:25.4732614Z         
2025-03-14T05:14:25.4732996Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4733496Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4733813Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:14:25.4734072Z         
2025-03-14T05:14:25.4734461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4735002Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:25.4735354Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:14:25.4735592Z         
2025-03-14T05:14:25.4736015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4736553Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:25.4736818Z         
2025-03-14T05:14:25.4737239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4737767Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:25.4738029Z         
2025-03-14T05:14:25.4738461Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4739020Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:25.4739354Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:14:25.4739722Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:25.4740090Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:14:25.4740351Z         
2025-03-14T05:14:25.4740796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4741954Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:25.4742296Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:14:25.4743088Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:25.4743472Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:14:25.4743750Z         
2025-03-14T05:14:25.4744254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4744795Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:25.4745146Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:25.4745975Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:14:25.4746565Z         
2025-03-14T05:14:25.4747521Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4748528Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:25.4749153Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:25.4749724Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:14:25.4750077Z         
2025-03-14T05:14:25.4750884Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4752172Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:25.4752876Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:25.4753314Z         
2025-03-14T05:14:25.4753940Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4754671Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:25.4755089Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:25.4755454Z         
2025-03-14T05:14:25.4756073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4756818Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:25.4757278Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:25.4757675Z         
2025-03-14T05:14:25.4758113Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4758622Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:25.4758978Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:25.4759248Z         
2025-03-14T05:14:25.4759712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4760367Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:25.4760691Z         
2025-03-14T05:14:25.4761139Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4761723Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:25.4762028Z         
2025-03-14T05:14:25.4762527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4763163Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:25.4763477Z         
2025-03-14T05:14:25.4763987Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4764677Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:14:25.4765019Z         
2025-03-14T05:14:25.4765560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4766249Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:14:25.4766623Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:25.4766982Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:25.4767252Z         
2025-03-14T05:14:25.4767755Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4768353Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:14:25.4768645Z         
2025-03-14T05:14:25.4769047Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4769567Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:25.4769840Z         
2025-03-14T05:14:25.4770239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4770746Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4771062Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4771397Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:14:25.4771672Z         
2025-03-14T05:14:25.4772088Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4772594Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4772923Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4773263Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:14:25.4773539Z         
2025-03-14T05:14:25.4773970Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4774466Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4774743Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:25.4775030Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:14:25.4775286Z         
2025-03-14T05:14:25.4775689Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4776215Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:25.4776532Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:25.4776810Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:14:25.4777067Z         
2025-03-14T05:14:25.4777474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4777990Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4778321Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:14:25.4778559Z         
2025-03-14T05:14:25.4778955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4779493Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4779822Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:14:25.4780061Z         
2025-03-14T05:14:25.4780456Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4780964Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4781348Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:14:25.4781731Z         
2025-03-14T05:14:25.4782144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4782700Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:25.4783064Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:14:25.4783306Z         
2025-03-14T05:14:25.4783751Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4784393Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:25.4784689Z         
2025-03-14T05:14:25.4785154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4785741Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:25.4786031Z         
2025-03-14T05:14:25.4786476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4787154Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:25.4787517Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:14:25.4787928Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:25.4788324Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:14:25.4788618Z         
2025-03-14T05:14:25.4789106Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4789712Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:25.4790071Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:14:25.4790444Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:25.4790832Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:14:25.4791124Z         
2025-03-14T05:14:25.4791599Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4792169Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:25.4792537Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:25.4792920Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:14:25.4793185Z         
2025-03-14T05:14:25.4793623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4794179Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:25.4794522Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:25.4794891Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:14:25.4795151Z         
2025-03-14T05:14:25.4795594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4796076Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:25.4796350Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:25.4796597Z         
2025-03-14T05:14:25.4797006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4797485Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:25.4797753Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:25.4797999Z         
2025-03-14T05:14:25.4798401Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4798897Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:25.4799210Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:25.4799469Z         
2025-03-14T05:14:25.4799874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4800364Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:25.4800692Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:25.4800952Z         
2025-03-14T05:14:25.4801405Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4802031Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:25.4802344Z         
2025-03-14T05:14:25.4802776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4803339Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:25.4803630Z         
2025-03-14T05:14:25.4804118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4804733Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:25.4805037Z         
2025-03-14T05:14:25.4805628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:25.4806336Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:25.4806608Z         
2025-03-14T05:14:25.4806994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4807507Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:14:25.4807771Z         
2025-03-14T05:14:25.4808282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.4808890Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:14:25.4809156Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:25.4809447Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:25.4809682Z         
2025-03-14T05:14:25.4810228Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.4810872Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.4811299Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:14:25.4811648Z         
2025-03-14T05:14:25.4812184Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.4812857Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.4813142Z         
2025-03-14T05:14:25.4813527Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4814002Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:25.4814248Z         
2025-03-14T05:14:25.4814780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.4815375Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:14:25.4815676Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:25.4815963Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:25.4816206Z         
2025-03-14T05:14:25.4816752Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.4817395Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.4817826Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:14:25.4818181Z         
2025-03-14T05:14:25.4818723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.4819395Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.4819681Z         
2025-03-14T05:14:25.4820066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4820544Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:25.4820793Z         
2025-03-14T05:14:25.4821317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.4821938Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:14:25.4822219Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:25.4822499Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:25.4822737Z         
2025-03-14T05:14:25.4823298Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.4823942Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.4824476Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:14:25.4824868Z         
2025-03-14T05:14:25.4825445Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.4826134Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.4826436Z         
2025-03-14T05:14:25.4826843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4827349Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:25.4827609Z         
2025-03-14T05:14:25.4828154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.4828818Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:14:25.4829111Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:25.4829424Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:25.4829677Z         
2025-03-14T05:14:25.4830250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.4830922Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.4831369Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:14:25.4831746Z         
2025-03-14T05:14:25.4832317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.4833017Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.4833319Z         
2025-03-14T05:14:25.4833709Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4834190Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:25.4834436Z         
2025-03-14T05:14:25.4834955Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.4835678Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:14:25.4835951Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:25.4836228Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:25.4836466Z         
2025-03-14T05:14:25.4837005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.4837690Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:25.4838148Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:14:25.4838502Z         
2025-03-14T05:14:25.4839046Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.4839717Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.4840000Z         
2025-03-14T05:14:25.4840385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4840864Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:25.4841103Z         
2025-03-14T05:14:25.4841472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.4842210Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:25.4842702Z         
2025-03-14T05:14:25.4843058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.4844406Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:25.4844993Z         
2025-03-14T05:14:25.4845366Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.4845900Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:14:25.4846220Z         
2025-03-14T05:14:25.4846698Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:25.4847285Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:25.4847545Z         
2025-03-14T05:14:25.4847930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:25.4848416Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:14:25.4848680Z         
2025-03-14T05:14:25.4849126Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:25.4849705Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:25.4849960Z         
2025-03-14T05:14:25.4850523Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:25.4851178Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:14:25.4851488Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:25.4851828Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:25.4852166Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:25.4852413Z         
2025-03-14T05:14:25.4852864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:25.4853399Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:25.4853634Z         
2025-03-14T05:14:25.4853919Z 
2025-03-14T05:14:25.4854015Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:25.4856189Z     def forward(self, L_features_p2_: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu", L_features_p3_: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu", L_features_p4_: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu", L_features_p5_: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu", L_features_p6_: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_: "f32[3, 4][4, 1]cpu", L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_: "f32[3, 4][4, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_weight_: "f32[256, 256, 3, 3][2304, 9, 3, 1]cpu", L_self_modules_rpn_head_modules_conv_parameters_bias_: "f32[256][1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_: "f32[3, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_: "f32[3][1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_: "f32[12, 256, 1, 1][256, 1, 1, 1]cpu", L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_: "f32[12][1]cpu"):
2025-03-14T05:14:25.4858454Z         l_features_p2_ = L_features_p2_
2025-03-14T05:14:25.4858681Z         l_features_p3_ = L_features_p3_
2025-03-14T05:14:25.4858897Z         l_features_p4_ = L_features_p4_
2025-03-14T05:14:25.4859113Z         l_features_p5_ = L_features_p5_
2025-03-14T05:14:25.4859320Z         l_features_p6_ = L_features_p6_
2025-03-14T05:14:25.4859706Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_0_
2025-03-14T05:14:25.4860255Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_1_
2025-03-14T05:14:25.4860794Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_2_
2025-03-14T05:14:25.4861330Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_3_
2025-03-14T05:14:25.4861860Z         l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = L_self_modules_anchor_generator_modules_cell_anchors_buffers_4_
2025-03-14T05:14:25.4862371Z         l_self_modules_rpn_head_modules_conv_parameters_weight_ = L_self_modules_rpn_head_modules_conv_parameters_weight_
2025-03-14T05:14:25.4862878Z         l_self_modules_rpn_head_modules_conv_parameters_bias_ = L_self_modules_rpn_head_modules_conv_parameters_bias_
2025-03-14T05:14:25.4863408Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_weight_
2025-03-14T05:14:25.4863984Z         l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = L_self_modules_rpn_head_modules_objectness_logits_parameters_bias_
2025-03-14T05:14:25.4864607Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_
2025-03-14T05:14:25.4865205Z         l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = L_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_
2025-03-14T05:14:25.4865588Z         
2025-03-14T05:14:25.4866145Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4866796Z         arange: "f32[304][1]cpu" = torch.arange(0.0, 1216, step = 4, dtype = torch.float32)
2025-03-14T05:14:25.4867074Z         
2025-03-14T05:14:25.4867463Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4867958Z         shifts_x: "f32[304][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:25.4868216Z         
2025-03-14T05:14:25.4868742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4869379Z         arange_1: "f32[296][1]cpu" = torch.arange(0.0, 1184, step = 4, dtype = torch.float32)
2025-03-14T05:14:25.4869653Z         
2025-03-14T05:14:25.4870053Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4870543Z         shifts_y: "f32[296][1]cpu" = arange_1.to(device(type='cpu'));  arange_1 = None
2025-03-14T05:14:25.4870816Z         
2025-03-14T05:14:25.4871308Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4871910Z         meshgrid = torch.functional.meshgrid(shifts_y, shifts_x);  shifts_y = shifts_x = None
2025-03-14T05:14:25.4872247Z         shift_y: "f32[296, 304][1, 0]cpu" = meshgrid[0]
2025-03-14T05:14:25.4872519Z         shift_x: "f32[296, 304][0, 1]cpu" = meshgrid[1];  meshgrid = None
2025-03-14T05:14:25.4872752Z         
2025-03-14T05:14:25.4873171Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4873688Z         shift_x_1: "f32[89984][1]cpu" = shift_x.reshape(-1);  shift_x = None
2025-03-14T05:14:25.4873940Z         
2025-03-14T05:14:25.4874356Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4874864Z         shift_y_1: "f32[89984][1]cpu" = shift_y.reshape(-1);  shift_y = None
2025-03-14T05:14:25.4875115Z         
2025-03-14T05:14:25.4875589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4876238Z         shifts: "f32[89984, 4][4, 1]cpu" = torch.stack((shift_x_1, shift_y_1, shift_x_1, shift_y_1), dim = 1);  shift_x_1 = shift_y_1 = None
2025-03-14T05:14:25.4876571Z         
2025-03-14T05:14:25.4877081Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4877709Z         view: "f32[89984, 1, 4][4, 4, 1]cpu" = shifts.view(-1, 1, 4);  shifts = None
2025-03-14T05:14:25.4878203Z         view_1: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_0_ = None
2025-03-14T05:14:25.4878695Z         add: "f32[89984, 3, 4][12, 4, 1]cpu" = view + view_1;  view = view_1 = None
2025-03-14T05:14:25.4878989Z         x: "f32[269952, 4][4, 1]cpu" = add.reshape(-1, 4);  add = None
2025-03-14T05:14:25.4879220Z         
2025-03-14T05:14:25.4879728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4880350Z         arange_2: "f32[152][1]cpu" = torch.arange(0.0, 1216, step = 8, dtype = torch.float32)
2025-03-14T05:14:25.4880618Z         
2025-03-14T05:14:25.4880991Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4881603Z         shifts_x_1: "f32[152][1]cpu" = arange_2.to(device(type='cpu'));  arange_2 = None
2025-03-14T05:14:25.4881876Z         
2025-03-14T05:14:25.4882390Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4883012Z         arange_3: "f32[148][1]cpu" = torch.arange(0.0, 1184, step = 8, dtype = torch.float32)
2025-03-14T05:14:25.4883279Z         
2025-03-14T05:14:25.4883653Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4884201Z         shifts_y_1: "f32[148][1]cpu" = arange_3.to(device(type='cpu'));  arange_3 = None
2025-03-14T05:14:25.4884469Z         
2025-03-14T05:14:25.4884921Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4885573Z         meshgrid_1 = torch.functional.meshgrid(shifts_y_1, shifts_x_1);  shifts_y_1 = shifts_x_1 = None
2025-03-14T05:14:25.4885926Z         shift_y_2: "f32[148, 152][1, 0]cpu" = meshgrid_1[0]
2025-03-14T05:14:25.4886202Z         shift_x_2: "f32[148, 152][0, 1]cpu" = meshgrid_1[1];  meshgrid_1 = None
2025-03-14T05:14:25.4886442Z         
2025-03-14T05:14:25.4886852Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4887360Z         shift_x_3: "f32[22496][1]cpu" = shift_x_2.reshape(-1);  shift_x_2 = None
2025-03-14T05:14:25.4887610Z         
2025-03-14T05:14:25.4888017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4888513Z         shift_y_3: "f32[22496][1]cpu" = shift_y_2.reshape(-1);  shift_y_2 = None
2025-03-14T05:14:25.4888755Z         
2025-03-14T05:14:25.4889210Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4889842Z         shifts_1: "f32[22496, 4][4, 1]cpu" = torch.stack((shift_x_3, shift_y_3, shift_x_3, shift_y_3), dim = 1);  shift_x_3 = shift_y_3 = None
2025-03-14T05:14:25.4890164Z         
2025-03-14T05:14:25.4890652Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4891263Z         view_2: "f32[22496, 1, 4][4, 4, 1]cpu" = shifts_1.view(-1, 1, 4);  shifts_1 = None
2025-03-14T05:14:25.4891754Z         view_3: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_1_ = None
2025-03-14T05:14:25.4892260Z         add_1: "f32[22496, 3, 4][12, 4, 1]cpu" = view_2 + view_3;  view_2 = view_3 = None
2025-03-14T05:14:25.4892563Z         x_1: "f32[67488, 4][4, 1]cpu" = add_1.reshape(-1, 4);  add_1 = None
2025-03-14T05:14:25.4892800Z         
2025-03-14T05:14:25.4893316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4893949Z         arange_4: "f32[76][1]cpu" = torch.arange(0.0, 1216, step = 16, dtype = torch.float32)
2025-03-14T05:14:25.4894211Z         
2025-03-14T05:14:25.4894580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4895066Z         shifts_x_2: "f32[76][1]cpu" = arange_4.to(device(type='cpu'));  arange_4 = None
2025-03-14T05:14:25.4895327Z         
2025-03-14T05:14:25.4895841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4896462Z         arange_5: "f32[74][1]cpu" = torch.arange(0.0, 1184, step = 16, dtype = torch.float32)
2025-03-14T05:14:25.4896727Z         
2025-03-14T05:14:25.4897121Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4897611Z         shifts_y_2: "f32[74][1]cpu" = arange_5.to(device(type='cpu'));  arange_5 = None
2025-03-14T05:14:25.4897868Z         
2025-03-14T05:14:25.4898327Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4898959Z         meshgrid_2 = torch.functional.meshgrid(shifts_y_2, shifts_x_2);  shifts_y_2 = shifts_x_2 = None
2025-03-14T05:14:25.4899308Z         shift_y_4: "f32[74, 76][1, 0]cpu" = meshgrid_2[0]
2025-03-14T05:14:25.4899580Z         shift_x_4: "f32[74, 76][0, 1]cpu" = meshgrid_2[1];  meshgrid_2 = None
2025-03-14T05:14:25.4899817Z         
2025-03-14T05:14:25.4900231Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4900747Z         shift_x_5: "f32[5624][1]cpu" = shift_x_4.reshape(-1);  shift_x_4 = None
2025-03-14T05:14:25.4900999Z         
2025-03-14T05:14:25.4901414Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4901919Z         shift_y_5: "f32[5624][1]cpu" = shift_y_4.reshape(-1);  shift_y_4 = None
2025-03-14T05:14:25.4902167Z         
2025-03-14T05:14:25.4902635Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4903283Z         shifts_2: "f32[5624, 4][4, 1]cpu" = torch.stack((shift_x_5, shift_y_5, shift_x_5, shift_y_5), dim = 1);  shift_x_5 = shift_y_5 = None
2025-03-14T05:14:25.4903612Z         
2025-03-14T05:14:25.4904111Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4904793Z         view_4: "f32[5624, 1, 4][4, 4, 1]cpu" = shifts_2.view(-1, 1, 4);  shifts_2 = None
2025-03-14T05:14:25.4905301Z         view_5: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_2_ = None
2025-03-14T05:14:25.4905826Z         add_2: "f32[5624, 3, 4][12, 4, 1]cpu" = view_4 + view_5;  view_4 = view_5 = None
2025-03-14T05:14:25.4906130Z         x_2: "f32[16872, 4][4, 1]cpu" = add_2.reshape(-1, 4);  add_2 = None
2025-03-14T05:14:25.4906371Z         
2025-03-14T05:14:25.4906890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4907518Z         arange_6: "f32[38][1]cpu" = torch.arange(0.0, 1216, step = 32, dtype = torch.float32)
2025-03-14T05:14:25.4907786Z         
2025-03-14T05:14:25.4908165Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4908650Z         shifts_x_3: "f32[38][1]cpu" = arange_6.to(device(type='cpu'));  arange_6 = None
2025-03-14T05:14:25.4908910Z         
2025-03-14T05:14:25.4909423Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4910047Z         arange_7: "f32[37][1]cpu" = torch.arange(0.0, 1184, step = 32, dtype = torch.float32)
2025-03-14T05:14:25.4910314Z         
2025-03-14T05:14:25.4910715Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4911201Z         shifts_y_3: "f32[37][1]cpu" = arange_7.to(device(type='cpu'));  arange_7 = None
2025-03-14T05:14:25.4911460Z         
2025-03-14T05:14:25.4911842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4912049Z         meshgrid_3 = torch.functional.meshgrid(shifts_y_3, shifts_x_3);  shifts_y_3 = shifts_x_3 = None
2025-03-14T05:14:25.4912158Z         shift_y_6: "f32[37, 38][1, 0]cpu" = meshgrid_3[0]
2025-03-14T05:14:25.4912275Z         shift_x_6: "f32[37, 38][0, 1]cpu" = meshgrid_3[1];  meshgrid_3 = None
2025-03-14T05:14:25.4912350Z         
2025-03-14T05:14:25.4912675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4912810Z         shift_x_7: "f32[1406][1]cpu" = shift_x_6.reshape(-1);  shift_x_6 = None
2025-03-14T05:14:25.4912876Z         
2025-03-14T05:14:25.4913213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4913337Z         shift_y_7: "f32[1406][1]cpu" = shift_y_6.reshape(-1);  shift_y_6 = None
2025-03-14T05:14:25.4913411Z         
2025-03-14T05:14:25.4913807Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4914023Z         shifts_3: "f32[1406, 4][4, 1]cpu" = torch.stack((shift_x_7, shift_y_7, shift_x_7, shift_y_7), dim = 1);  shift_x_7 = shift_y_7 = None
2025-03-14T05:14:25.4914086Z         
2025-03-14T05:14:25.4914498Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4915318Z         view_6: "f32[1406, 1, 4][4, 4, 1]cpu" = shifts_3.view(-1, 1, 4);  shifts_3 = None
2025-03-14T05:14:25.4915625Z         view_7: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_3_ = None
2025-03-14T05:14:25.4915763Z         add_3: "f32[1406, 3, 4][12, 4, 1]cpu" = view_6 + view_7;  view_6 = view_7 = None
2025-03-14T05:14:25.4915882Z         x_3: "f32[4218, 4][4, 1]cpu" = add_3.reshape(-1, 4);  add_3 = None
2025-03-14T05:14:25.4915945Z         
2025-03-14T05:14:25.4916364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:44 in _create_grid_offsets, code: torch.arange(offset * stride, grid_width * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4916508Z         arange_8: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:25.4916578Z         
2025-03-14T05:14:25.4916860Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4917002Z         shifts_x_4: "f32[19][1]cpu" = arange_8.to(device(type='cpu'));  arange_8 = None
2025-03-14T05:14:25.4917068Z         
2025-03-14T05:14:25.4917484Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:48 in _create_grid_offsets, code: torch.arange(offset * stride, grid_height * stride, step=stride, dtype=torch.float32),
2025-03-14T05:14:25.4917632Z         arange_9: "f32[19][1]cpu" = torch.arange(0.0, 1216, step = 64, dtype = torch.float32)
2025-03-14T05:14:25.4917696Z         
2025-03-14T05:14:25.4918001Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.4918133Z         shifts_y_4: "f32[19][1]cpu" = arange_9.to(device(type='cpu'));  arange_9 = None
2025-03-14T05:14:25.4918202Z         
2025-03-14T05:14:25.4918559Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:52 in _create_grid_offsets, code: shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
2025-03-14T05:14:25.4918767Z         meshgrid_4 = torch.functional.meshgrid(shifts_y_4, shifts_x_4);  shifts_y_4 = shifts_x_4 = None
2025-03-14T05:14:25.4918866Z         shift_y_8: "f32[19, 19][1, 0]cpu" = meshgrid_4[0]
2025-03-14T05:14:25.4918989Z         shift_x_8: "f32[19, 19][0, 1]cpu" = meshgrid_4[1];  meshgrid_4 = None
2025-03-14T05:14:25.4919053Z         
2025-03-14T05:14:25.4919374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:53 in _create_grid_offsets, code: shift_x = shift_x.reshape(-1)
2025-03-14T05:14:25.4919496Z         shift_x_9: "f32[361][1]cpu" = shift_x_8.reshape(-1);  shift_x_8 = None
2025-03-14T05:14:25.4919570Z         
2025-03-14T05:14:25.4919882Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:54 in _create_grid_offsets, code: shift_y = shift_y.reshape(-1)
2025-03-14T05:14:25.4920007Z         shift_y_9: "f32[361][1]cpu" = shift_y_8.reshape(-1);  shift_y_8 = None
2025-03-14T05:14:25.4920073Z         
2025-03-14T05:14:25.4920447Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:175 in _grid_anchors, code: shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
2025-03-14T05:14:25.4920651Z         shifts_4: "f32[361, 4][4, 1]cpu" = torch.stack((shift_x_9, shift_y_9, shift_x_9, shift_y_9), dim = 1);  shift_x_9 = shift_y_9 = None
2025-03-14T05:14:25.4920721Z         
2025-03-14T05:14:25.4921118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/anchor_generator.py:177 in _grid_anchors, code: anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
2025-03-14T05:14:25.4921265Z         view_8: "f32[361, 1, 4][4, 4, 1]cpu" = shifts_4.view(-1, 1, 4);  shifts_4 = None
2025-03-14T05:14:25.4921559Z         view_9: "f32[1, 3, 4][12, 4, 1]cpu" = l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_.view(1, -1, 4);  l_self_modules_anchor_generator_modules_cell_anchors_buffers_4_ = None
2025-03-14T05:14:25.4921704Z         add_4: "f32[361, 3, 4][12, 4, 1]cpu" = view_8 + view_9;  view_8 = view_9 = None
2025-03-14T05:14:25.4921816Z         x_4: "f32[1083, 4][4, 1]cpu" = add_4.reshape(-1, 4);  add_4 = None
2025-03-14T05:14:25.4921891Z         
2025-03-14T05:14:25.4922179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:25.4922310Z         tensor: "f32[269952, 4][4, 1]cpu" = x.to(torch.float32);  x = None
2025-03-14T05:14:25.4922435Z         tensor_1: "f32[67488, 4][4, 1]cpu" = x_1.to(torch.float32);  x_1 = None
2025-03-14T05:14:25.4922560Z         tensor_2: "f32[16872, 4][4, 1]cpu" = x_2.to(torch.float32);  x_2 = None
2025-03-14T05:14:25.4922675Z         tensor_3: "f32[4218, 4][4, 1]cpu" = x_3.to(torch.float32);  x_3 = None
2025-03-14T05:14:25.4922794Z         tensor_4: "f32[1083, 4][4, 1]cpu" = x_4.to(torch.float32);  x_4 = None
2025-03-14T05:14:25.4922859Z         
2025-03-14T05:14:25.4923118Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4923528Z         x_5: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.conv2d(l_features_p2_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p2_ = None
2025-03-14T05:14:25.4923602Z         
2025-03-14T05:14:25.4924350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4924596Z         x_6: "f32[4, 256, 296, 304][23035904, 89984, 304, 1]cpu" = torch.nn.functional.relu(x_5, inplace = False);  x_5 = None
2025-03-14T05:14:25.4924702Z         
2025-03-14T05:14:25.4925096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4925529Z         score: "f32[4, 3, 296, 304][269952, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4925594Z         
2025-03-14T05:14:25.4925959Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4926365Z         x_15: "f32[4, 12, 296, 304][1079808, 89984, 304, 1]cpu" = torch.conv2d(x_6, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_6 = None
2025-03-14T05:14:25.4926443Z         
2025-03-14T05:14:25.4926699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4927108Z         x_7: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.conv2d(l_features_p3_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p3_ = None
2025-03-14T05:14:25.4927174Z         
2025-03-14T05:14:25.4927452Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4927653Z         x_8: "f32[4, 256, 148, 152][5758976, 22496, 152, 1]cpu" = torch.nn.functional.relu(x_7, inplace = False);  x_7 = None
2025-03-14T05:14:25.4927731Z         
2025-03-14T05:14:25.4928096Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4928512Z         score_1: "f32[4, 3, 148, 152][67488, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4928586Z         
2025-03-14T05:14:25.4928933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4929324Z         x_16: "f32[4, 12, 148, 152][269952, 22496, 152, 1]cpu" = torch.conv2d(x_8, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_8 = None
2025-03-14T05:14:25.4929389Z         
2025-03-14T05:14:25.4929646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4930030Z         x_9: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.conv2d(l_features_p4_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p4_ = None
2025-03-14T05:14:25.4930104Z         
2025-03-14T05:14:25.4930369Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4930550Z         x_10: "f32[4, 256, 74, 76][1439744, 5624, 76, 1]cpu" = torch.nn.functional.relu(x_9, inplace = False);  x_9 = None
2025-03-14T05:14:25.4930634Z         
2025-03-14T05:14:25.4931003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4931401Z         score_2: "f32[4, 3, 74, 76][16872, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4931476Z         
2025-03-14T05:14:25.4931820Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4932206Z         x_17: "f32[4, 12, 74, 76][67488, 5624, 76, 1]cpu" = torch.conv2d(x_10, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_10 = None
2025-03-14T05:14:25.4932280Z         
2025-03-14T05:14:25.4932525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4932906Z         x_11: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.conv2d(l_features_p5_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p5_ = None
2025-03-14T05:14:25.4932969Z         
2025-03-14T05:14:25.4933240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4933411Z         x_12: "f32[4, 256, 37, 38][359936, 1406, 38, 1]cpu" = torch.nn.functional.relu(x_11, inplace = False);  x_11 = None
2025-03-14T05:14:25.4933485Z         
2025-03-14T05:14:25.4933864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4934245Z         score_3: "f32[4, 3, 37, 38][4218, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1)
2025-03-14T05:14:25.4934310Z         
2025-03-14T05:14:25.4934674Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4935049Z         x_18: "f32[4, 12, 37, 38][16872, 1406, 38, 1]cpu" = torch.conv2d(x_12, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_12 = None
2025-03-14T05:14:25.4935121Z         
2025-03-14T05:14:25.4935367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:142 in forward, code: x = F.conv2d(
2025-03-14T05:14:25.4935927Z         x_13: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.conv2d(l_features_p6_, l_self_modules_rpn_head_modules_conv_parameters_weight_, l_self_modules_rpn_head_modules_conv_parameters_bias_, (1, 1), (1, 1), (1, 1), 1);  l_features_p6_ = l_self_modules_rpn_head_modules_conv_parameters_weight_ = l_self_modules_rpn_head_modules_conv_parameters_bias_ = None
2025-03-14T05:14:25.4935999Z         
2025-03-14T05:14:25.4936262Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:148 in forward, code: x = self.activation(x)
2025-03-14T05:14:25.4936433Z         x_14: "f32[4, 256, 19, 19][92416, 361, 19, 1]cpu" = torch.nn.functional.relu(x_13, inplace = False);  x_13 = None
2025-03-14T05:14:25.4936496Z         
2025-03-14T05:14:25.4936881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:175 in forward, code: pred_objectness_logits.append(self.objectness_logits(t))
2025-03-14T05:14:25.4937491Z         score_4: "f32[4, 3, 19, 19][1083, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_, l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  l_self_modules_rpn_head_modules_objectness_logits_parameters_weight_ = l_self_modules_rpn_head_modules_objectness_logits_parameters_bias_ = None
2025-03-14T05:14:25.4937582Z         
2025-03-14T05:14:25.4937925Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:176 in forward, code: pred_anchor_deltas.append(self.anchor_deltas(t))
2025-03-14T05:14:25.4938509Z         x_19: "f32[4, 12, 19, 19][4332, 361, 19, 1]cpu" = torch.conv2d(x_14, l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_, l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_, (1, 1), (0, 0), (1, 1), 1);  x_14 = l_self_modules_rpn_head_modules_anchor_deltas_parameters_weight_ = l_self_modules_rpn_head_modules_anchor_deltas_parameters_bias_ = None
2025-03-14T05:14:25.4938575Z         
2025-03-14T05:14:25.4938916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:458 in <listcomp>, code: score.permute(0, 2, 3, 1).flatten(1)
2025-03-14T05:14:25.4939080Z         permute: "f32[4, 296, 304, 3][269952, 304, 1, 89984]cpu" = score.permute(0, 2, 3, 1);  score = None
2025-03-14T05:14:25.4939232Z         logits_i: "f32[4, 269952][269952, 1]cpu" = permute.flatten(1);  permute = None
2025-03-14T05:14:25.4939397Z         permute_1: "f32[4, 148, 152, 3][67488, 152, 1, 22496]cpu" = score_1.permute(0, 2, 3, 1);  score_1 = None
2025-03-14T05:14:25.4939553Z         logits_i_1: "f32[4, 67488][67488, 1]cpu" = permute_1.flatten(1);  permute_1 = None
2025-03-14T05:14:25.4939733Z         permute_2: "f32[4, 74, 76, 3][16872, 76, 1, 5624]cpu" = score_2.permute(0, 2, 3, 1);  score_2 = None
2025-03-14T05:14:25.4939873Z         logits_i_2: "f32[4, 16872][16872, 1]cpu" = permute_2.flatten(1);  permute_2 = None
2025-03-14T05:14:25.4940028Z         permute_3: "f32[4, 37, 38, 3][4218, 38, 1, 1406]cpu" = score_3.permute(0, 2, 3, 1);  score_3 = None
2025-03-14T05:14:25.4940165Z         logits_i_3: "f32[4, 4218][4218, 1]cpu" = permute_3.flatten(1);  permute_3 = None
2025-03-14T05:14:25.4940317Z         permute_4: "f32[4, 19, 19, 3][1083, 19, 1, 361]cpu" = score_4.permute(0, 2, 3, 1);  score_4 = None
2025-03-14T05:14:25.4940463Z         logits_i_4: "f32[4, 1083][1083, 1]cpu" = permute_4.flatten(1);  permute_4 = None
2025-03-14T05:14:25.4940539Z         
2025-03-14T05:14:25.4940961Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:463 in <listcomp>, code: x.view(x.shape[0], -1, self.anchor_generator.box_dim, x.shape[-2], x.shape[-1])
2025-03-14T05:14:25.4941149Z         view_10: "f32[4, 3, 4, 296, 304][1079808, 359936, 89984, 304, 1]cpu" = x_15.view(4, -1, 4, 296, 304);  x_15 = None
2025-03-14T05:14:25.4941333Z         permute_5: "f32[4, 296, 304, 3, 4][1079808, 304, 1, 359936, 89984]cpu" = view_10.permute(0, 3, 4, 1, 2);  view_10 = None
2025-03-14T05:14:25.4941521Z         pred_anchor_deltas_i: "f32[4, 269952, 4][1079808, 4, 1]cpu" = permute_5.flatten(1, -2);  permute_5 = None
2025-03-14T05:14:25.4941681Z         view_11: "f32[4, 3, 4, 148, 152][269952, 89984, 22496, 152, 1]cpu" = x_16.view(4, -1, 4, 148, 152);  x_16 = None
2025-03-14T05:14:25.4941865Z         permute_6: "f32[4, 148, 152, 3, 4][269952, 152, 1, 89984, 22496]cpu" = view_11.permute(0, 3, 4, 1, 2);  view_11 = None
2025-03-14T05:14:25.4942035Z         pred_anchor_deltas_i_2: "f32[4, 67488, 4][269952, 4, 1]cpu" = permute_6.flatten(1, -2);  permute_6 = None
2025-03-14T05:14:25.4942190Z         view_12: "f32[4, 3, 4, 74, 76][67488, 22496, 5624, 76, 1]cpu" = x_17.view(4, -1, 4, 74, 76);  x_17 = None
2025-03-14T05:14:25.4942373Z         permute_7: "f32[4, 74, 76, 3, 4][67488, 76, 1, 22496, 5624]cpu" = view_12.permute(0, 3, 4, 1, 2);  view_12 = None
2025-03-14T05:14:25.4942550Z         pred_anchor_deltas_i_4: "f32[4, 16872, 4][67488, 4, 1]cpu" = permute_7.flatten(1, -2);  permute_7 = None
2025-03-14T05:14:25.4942710Z         view_13: "f32[4, 3, 4, 37, 38][16872, 5624, 1406, 38, 1]cpu" = x_18.view(4, -1, 4, 37, 38);  x_18 = None
2025-03-14T05:14:25.4942878Z         permute_8: "f32[4, 37, 38, 3, 4][16872, 38, 1, 5624, 1406]cpu" = view_13.permute(0, 3, 4, 1, 2);  view_13 = None
2025-03-14T05:14:25.4943046Z         pred_anchor_deltas_i_6: "f32[4, 4218, 4][16872, 4, 1]cpu" = permute_8.flatten(1, -2);  permute_8 = None
2025-03-14T05:14:25.4943195Z         view_14: "f32[4, 3, 4, 19, 19][4332, 1444, 361, 19, 1]cpu" = x_19.view(4, -1, 4, 19, 19);  x_19 = None
2025-03-14T05:14:25.4943354Z         permute_9: "f32[4, 19, 19, 3, 4][4332, 19, 1, 1444, 361]cpu" = view_14.permute(0, 3, 4, 1, 2);  view_14 = None
2025-03-14T05:14:25.4943529Z         pred_anchor_deltas_i_8: "f32[4, 1083, 4][4332, 4, 1]cpu" = permute_9.flatten(1, -2);  permute_9 = None
2025-03-14T05:14:25.4943594Z         
2025-03-14T05:14:25.4944006Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4944265Z         pred_anchor_deltas_i_1: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.reshape(-1, 4);  pred_anchor_deltas_i = None
2025-03-14T05:14:25.4944338Z         
2025-03-14T05:14:25.4944782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4944939Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = tensor.unsqueeze(0);  tensor = None
2025-03-14T05:14:25.4945115Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:25.4945255Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:25.4945330Z         
2025-03-14T05:14:25.4945700Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4945882Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:25.4945948Z         
2025-03-14T05:14:25.4946292Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4946437Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:25.4946509Z         
2025-03-14T05:14:25.4946826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4946966Z         getitem_10: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4947098Z         getitem_11: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4947257Z         widths: "f32[1079808][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:25.4947324Z         
2025-03-14T05:14:25.4947647Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4947773Z         getitem_12: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4947902Z         getitem_13: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4948055Z         heights: "f32[1079808][1]cpu" = getitem_12 - getitem_13;  getitem_12 = getitem_13 = None
2025-03-14T05:14:25.4948130Z         
2025-03-14T05:14:25.4948458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4948613Z         getitem_14: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4948704Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:25.4948840Z         ctr_x: "f32[1079808][1]cpu" = getitem_14 + mul;  getitem_14 = mul = None
2025-03-14T05:14:25.4948907Z         
2025-03-14T05:14:25.4949226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4949374Z         getitem_15: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:25.4949473Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:25.4949607Z         ctr_y: "f32[1079808][1]cpu" = getitem_15 + mul_1;  getitem_15 = mul_1 = None
2025-03-14T05:14:25.4949680Z         
2025-03-14T05:14:25.4950005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4950175Z         getitem_16: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4950293Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_16 / 1.0;  getitem_16 = None
2025-03-14T05:14:25.4950367Z         
2025-03-14T05:14:25.4950668Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4950832Z         getitem_17: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4950956Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_17 / 1.0;  getitem_17 = None
2025-03-14T05:14:25.4951045Z         
2025-03-14T05:14:25.4951352Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4951505Z         getitem_18: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4951629Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_18 / 1.0;  getitem_18 = None
2025-03-14T05:14:25.4951694Z         
2025-03-14T05:14:25.4952017Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4952203Z         getitem_19: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:25.4952320Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_19 / 1.0;  getitem_19 = None
2025-03-14T05:14:25.4952384Z         
2025-03-14T05:14:25.4952729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4952875Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:25.4952951Z         
2025-03-14T05:14:25.4953282Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4953429Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:25.4953495Z         
2025-03-14T05:14:25.4953846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4953985Z         getitem_20: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:25.4954136Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_20;  dx = getitem_20 = None
2025-03-14T05:14:25.4954291Z         getitem_21: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:25.4954443Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_21;  mul_2 = getitem_21 = None
2025-03-14T05:14:25.4954526Z         
2025-03-14T05:14:25.4954879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4955022Z         getitem_22: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:25.4955156Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_22;  dy = getitem_22 = None
2025-03-14T05:14:25.4955307Z         getitem_23: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:25.4955457Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_23;  mul_3 = getitem_23 = None
2025-03-14T05:14:25.4955523Z         
2025-03-14T05:14:25.4955861Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4955982Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:25.4956151Z         getitem_24: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:25.4956286Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_24;  exp = getitem_24 = None
2025-03-14T05:14:25.4956362Z         
2025-03-14T05:14:25.4956692Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4956820Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:25.4957011Z         getitem_25: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:25.4957158Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_25;  exp_1 = getitem_25 = None
2025-03-14T05:14:25.4957225Z         
2025-03-14T05:14:25.4957541Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4957647Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:25.4957783Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:25.4957858Z         
2025-03-14T05:14:25.4958161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4958268Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:25.4958388Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:25.4958462Z         
2025-03-14T05:14:25.4958763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4958887Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:25.4959017Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:25.4959091Z         
2025-03-14T05:14:25.4959389Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4959508Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:25.4959636Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:25.4959710Z         
2025-03-14T05:14:25.4960082Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4960267Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:25.4960348Z         
2025-03-14T05:14:25.4960675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4960837Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:25.4960907Z         
2025-03-14T05:14:25.4961273Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4961455Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:25.4961521Z         
2025-03-14T05:14:25.4961912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4962117Z         pred_anchor_deltas_i_3: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_2.reshape(-1, 4);  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:25.4962188Z         
2025-03-14T05:14:25.4962604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4962765Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = tensor_1.unsqueeze(0);  tensor_1 = None
2025-03-14T05:14:25.4962914Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:25.4963076Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:25.4963140Z         
2025-03-14T05:14:25.4963506Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4963677Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:25.4963750Z         
2025-03-14T05:14:25.4964073Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4964225Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:25.4964288Z         
2025-03-14T05:14:25.4964601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4964732Z         getitem_26: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4964867Z         getitem_27: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4965024Z         widths_1: "f32[269952][1]cpu" = getitem_26 - getitem_27;  getitem_26 = getitem_27 = None
2025-03-14T05:14:25.4965089Z         
2025-03-14T05:14:25.4965407Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4965532Z         getitem_28: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4965656Z         getitem_29: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4965803Z         heights_1: "f32[269952][1]cpu" = getitem_28 - getitem_29;  getitem_28 = getitem_29 = None
2025-03-14T05:14:25.4965874Z         
2025-03-14T05:14:25.4966195Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4966341Z         getitem_30: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4966435Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:25.4966573Z         ctr_x_1: "f32[269952][1]cpu" = getitem_30 + mul_10;  getitem_30 = mul_10 = None
2025-03-14T05:14:25.4966638Z         
2025-03-14T05:14:25.4966948Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4967093Z         getitem_31: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:25.4967194Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:25.4967326Z         ctr_y_1: "f32[269952][1]cpu" = getitem_31 + mul_11;  getitem_31 = mul_11 = None
2025-03-14T05:14:25.4967399Z         
2025-03-14T05:14:25.4967696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4967857Z         getitem_32: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4967970Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_32 / 1.0;  getitem_32 = None
2025-03-14T05:14:25.4968042Z         
2025-03-14T05:14:25.4968342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4968501Z         getitem_33: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4968616Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_33 / 1.0;  getitem_33 = None
2025-03-14T05:14:25.4968718Z         
2025-03-14T05:14:25.4969027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4969182Z         getitem_34: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4969294Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_34 / 1.0;  getitem_34 = None
2025-03-14T05:14:25.4969368Z         
2025-03-14T05:14:25.4969676Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4969865Z         getitem_35: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:25.4969975Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_35 / 1.0;  getitem_35 = None
2025-03-14T05:14:25.4970051Z         
2025-03-14T05:14:25.4970375Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4970521Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:25.4970587Z         
2025-03-14T05:14:25.4970916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4971052Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:25.4971121Z         
2025-03-14T05:14:25.4971450Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4971595Z         getitem_36: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:25.4971744Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_36;  dx_1 = getitem_36 = None
2025-03-14T05:14:25.4971901Z         getitem_37: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:25.4972160Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_37;  mul_12 = getitem_37 = None
2025-03-14T05:14:25.4972225Z         
2025-03-14T05:14:25.4972591Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4972729Z         getitem_38: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:25.4972859Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_38;  dy_1 = getitem_38 = None
2025-03-14T05:14:25.4973011Z         getitem_39: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:25.4973161Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_39;  mul_13 = getitem_39 = None
2025-03-14T05:14:25.4973225Z         
2025-03-14T05:14:25.4973560Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4973677Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:25.4973848Z         getitem_40: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:25.4973984Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_40;  exp_2 = getitem_40 = None
2025-03-14T05:14:25.4974057Z         
2025-03-14T05:14:25.4974382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4974517Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:25.4974680Z         getitem_41: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:25.4974819Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_41;  exp_3 = getitem_41 = None
2025-03-14T05:14:25.4974885Z         
2025-03-14T05:14:25.4975188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4975302Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:25.4975429Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:25.4975492Z         
2025-03-14T05:14:25.4975796Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4975894Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:25.4976015Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:25.4976079Z         
2025-03-14T05:14:25.4976383Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4976500Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:25.4976640Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:25.4976705Z         
2025-03-14T05:14:25.4977005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4977119Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:25.4977256Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:25.4977338Z         
2025-03-14T05:14:25.4977686Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4977890Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:25.4977964Z         
2025-03-14T05:14:25.4978287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4978462Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:25.4978527Z         
2025-03-14T05:14:25.4978909Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4979090Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:25.4979154Z         
2025-03-14T05:14:25.4979550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4979755Z         pred_anchor_deltas_i_5: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_4.reshape(-1, 4);  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:25.4979827Z         
2025-03-14T05:14:25.4980250Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4980406Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = tensor_2.unsqueeze(0);  tensor_2 = None
2025-03-14T05:14:25.4980572Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:25.4980715Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:25.4980782Z         
2025-03-14T05:14:25.4981149Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4981330Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_5.float();  pred_anchor_deltas_i_5 = None
2025-03-14T05:14:25.4981401Z         
2025-03-14T05:14:25.4982015Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.4982169Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:25.4982237Z         
2025-03-14T05:14:25.4982550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.4982681Z         getitem_42: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:25.4982813Z         getitem_43: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4982962Z         widths_2: "f32[67488][1]cpu" = getitem_42 - getitem_43;  getitem_42 = getitem_43 = None
2025-03-14T05:14:25.4983035Z         
2025-03-14T05:14:25.4983348Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.4983479Z         getitem_44: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:25.4983600Z         getitem_45: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:25.4983820Z         heights_2: "f32[67488][1]cpu" = getitem_44 - getitem_45;  getitem_44 = getitem_45 = None
2025-03-14T05:14:25.4983888Z         
2025-03-14T05:14:25.4984307Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.4984479Z         getitem_46: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:25.4984587Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:25.4984724Z         ctr_x_2: "f32[67488][1]cpu" = getitem_46 + mul_20;  getitem_46 = mul_20 = None
2025-03-14T05:14:25.4984803Z         
2025-03-14T05:14:25.4985122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.4985284Z         getitem_47: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:25.4985385Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:25.4985531Z         ctr_y_2: "f32[67488][1]cpu" = getitem_47 + mul_21;  getitem_47 = mul_21 = None
2025-03-14T05:14:25.4985600Z         
2025-03-14T05:14:25.4985923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.4986084Z         getitem_48: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.4986212Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_48 / 1.0;  getitem_48 = None
2025-03-14T05:14:25.4986278Z         
2025-03-14T05:14:25.4986595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.4986758Z         getitem_49: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.4986904Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_49 / 1.0;  getitem_49 = None
2025-03-14T05:14:25.4986981Z         
2025-03-14T05:14:25.4987287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.4987450Z         getitem_50: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.4987562Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_50 / 1.0;  getitem_50 = None
2025-03-14T05:14:25.4987662Z         
2025-03-14T05:14:25.4987974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.4988173Z         getitem_51: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:25.4988288Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_51 / 1.0;  getitem_51 = None
2025-03-14T05:14:25.4988376Z         
2025-03-14T05:14:25.4988720Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.4988872Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:25.4988938Z         
2025-03-14T05:14:25.4989284Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.4989422Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:25.4989496Z         
2025-03-14T05:14:25.4989844Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.4990007Z         getitem_52: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:25.4990138Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_52;  dx_2 = getitem_52 = None
2025-03-14T05:14:25.4990337Z         getitem_53: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:25.4990481Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_53;  mul_22 = getitem_53 = None
2025-03-14T05:14:25.4990559Z         
2025-03-14T05:14:25.4990917Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.4991067Z         getitem_54: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:25.4991192Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_54;  dy_2 = getitem_54 = None
2025-03-14T05:14:25.4991359Z         getitem_55: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:25.4991498Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_55;  mul_23 = getitem_55 = None
2025-03-14T05:14:25.4991573Z         
2025-03-14T05:14:25.4991916Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.4992045Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:25.4992215Z         getitem_56: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:25.4992365Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_56;  exp_4 = getitem_56 = None
2025-03-14T05:14:25.4992431Z         
2025-03-14T05:14:25.4992779Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.4992927Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:25.4993099Z         getitem_57: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:25.4993243Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_57;  exp_5 = getitem_57 = None
2025-03-14T05:14:25.4993309Z         
2025-03-14T05:14:25.4993651Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.4993756Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:25.4993883Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:25.4993950Z         
2025-03-14T05:14:25.4994276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.4994380Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:25.4994504Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:25.4994571Z         
2025-03-14T05:14:25.4994899Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.4995019Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:25.4995164Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:25.4995233Z         
2025-03-14T05:14:25.4995554Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.4995671Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:25.4995832Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:25.4995899Z         
2025-03-14T05:14:25.4996264Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.4996477Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:25.4996550Z         
2025-03-14T05:14:25.4996893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.4997064Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:25.4997131Z         
2025-03-14T05:14:25.4997543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.4997713Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:25.4997785Z         
2025-03-14T05:14:25.4998172Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.4998380Z         pred_anchor_deltas_i_7: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_6.reshape(-1, 4);  pred_anchor_deltas_i_6 = None
2025-03-14T05:14:25.4998443Z         
2025-03-14T05:14:25.4998868Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.4999037Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = tensor_3.unsqueeze(0);  tensor_3 = None
2025-03-14T05:14:25.4999192Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:25.4999327Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:25.4999399Z         
2025-03-14T05:14:25.4999772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.4999946Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_7.float();  pred_anchor_deltas_i_7 = None
2025-03-14T05:14:25.5000010Z         
2025-03-14T05:14:25.5000320Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.5000473Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:25.5000537Z         
2025-03-14T05:14:25.5000850Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.5000978Z         getitem_58: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:25.5001109Z         getitem_59: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:25.5001255Z         widths_3: "f32[16872][1]cpu" = getitem_58 - getitem_59;  getitem_58 = getitem_59 = None
2025-03-14T05:14:25.5001325Z         
2025-03-14T05:14:25.5001632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.5001759Z         getitem_60: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:25.5001896Z         getitem_61: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:25.5002051Z         heights_3: "f32[16872][1]cpu" = getitem_60 - getitem_61;  getitem_60 = getitem_61 = None
2025-03-14T05:14:25.5002115Z         
2025-03-14T05:14:25.5002438Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.5002557Z         getitem_62: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:25.5002658Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:25.5002785Z         ctr_x_3: "f32[16872][1]cpu" = getitem_62 + mul_30;  getitem_62 = mul_30 = None
2025-03-14T05:14:25.5002857Z         
2025-03-14T05:14:25.5003155Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.5003307Z         getitem_63: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:25.5003399Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:25.5003531Z         ctr_y_3: "f32[16872][1]cpu" = getitem_63 + mul_31;  getitem_63 = mul_31 = None
2025-03-14T05:14:25.5003596Z         
2025-03-14T05:14:25.5003892Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.5004040Z         getitem_64: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.5004158Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_64 / 1.0;  getitem_64 = None
2025-03-14T05:14:25.5004223Z         
2025-03-14T05:14:25.5004517Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.5004684Z         getitem_65: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.5004801Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_65 / 1.0;  getitem_65 = None
2025-03-14T05:14:25.5004865Z         
2025-03-14T05:14:25.5005164Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.5005309Z         getitem_66: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.5005450Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_66 / 1.0;  getitem_66 = None
2025-03-14T05:14:25.5005515Z         
2025-03-14T05:14:25.5005822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.5006004Z         getitem_67: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:25.5006123Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_67 / 1.0;  getitem_67 = None
2025-03-14T05:14:25.5006186Z         
2025-03-14T05:14:25.5006525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.5006669Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:25.5006735Z         
2025-03-14T05:14:25.5007066Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.5007197Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:25.5007269Z         
2025-03-14T05:14:25.5007619Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.5007761Z         getitem_68: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:25.5007883Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_68;  dx_3 = getitem_68 = None
2025-03-14T05:14:25.5008061Z         getitem_69: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:25.5008195Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_69;  mul_32 = getitem_69 = None
2025-03-14T05:14:25.5008268Z         
2025-03-14T05:14:25.5008601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.5008740Z         getitem_70: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:25.5008859Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_70;  dy_3 = getitem_70 = None
2025-03-14T05:14:25.5009016Z         getitem_71: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:25.5009150Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_71;  mul_33 = getitem_71 = None
2025-03-14T05:14:25.5009222Z         
2025-03-14T05:14:25.5009538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.5009660Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:25.5009813Z         getitem_72: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:25.5009955Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_72;  exp_6 = getitem_72 = None
2025-03-14T05:14:25.5010019Z         
2025-03-14T05:14:25.5010350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.5010481Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:25.5010649Z         getitem_73: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:25.5010786Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_73;  exp_7 = getitem_73 = None
2025-03-14T05:14:25.5010869Z         
2025-03-14T05:14:25.5011187Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.5011291Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:25.5011406Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:25.5011478Z         
2025-03-14T05:14:25.5011781Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.5011887Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:25.5012001Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:25.5012077Z         
2025-03-14T05:14:25.5012379Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.5012506Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:25.5012650Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:25.5012725Z         
2025-03-14T05:14:25.5013019Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.5013168Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:25.5013297Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:25.5013371Z         
2025-03-14T05:14:25.5013723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.5013918Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:25.5013982Z         
2025-03-14T05:14:25.5014314Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.5014480Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:25.5014546Z         
2025-03-14T05:14:25.5014922Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.5015089Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:25.5015164Z         
2025-03-14T05:14:25.5015549Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:25.5015755Z         pred_anchor_deltas_i_9: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_8.reshape(-1, 4);  pred_anchor_deltas_i_8 = None
2025-03-14T05:14:25.5015818Z         
2025-03-14T05:14:25.5016253Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:25.5016419Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = tensor_4.unsqueeze(0);  tensor_4 = None
2025-03-14T05:14:25.5016576Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:25.5016713Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:25.5016786Z         
2025-03-14T05:14:25.5017173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:25.5017346Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_9.float();  pred_anchor_deltas_i_9 = None
2025-03-14T05:14:25.5017411Z         
2025-03-14T05:14:25.5017729Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:25.5017874Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:25.5017946Z         
2025-03-14T05:14:25.5018256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:25.5018391Z         getitem_74: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:25.5018513Z         getitem_75: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:25.5018668Z         widths_4: "f32[4332][1]cpu" = getitem_74 - getitem_75;  getitem_74 = getitem_75 = None
2025-03-14T05:14:25.5018734Z         
2025-03-14T05:14:25.5019059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:25.5019182Z         getitem_76: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:25.5019325Z         getitem_77: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:25.5019474Z         heights_4: "f32[4332][1]cpu" = getitem_76 - getitem_77;  getitem_76 = getitem_77 = None
2025-03-14T05:14:25.5019567Z         
2025-03-14T05:14:25.5019878Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:25.5020008Z         getitem_78: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:25.5020100Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:25.5020238Z         ctr_x_4: "f32[4332][1]cpu" = getitem_78 + mul_40;  getitem_78 = mul_40 = None
2025-03-14T05:14:25.5020303Z         
2025-03-14T05:14:25.5020620Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:25.5020768Z         getitem_79: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:25.5020869Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:25.5020999Z         ctr_y_4: "f32[4332][1]cpu" = getitem_79 + mul_41;  getitem_79 = mul_41 = None
2025-03-14T05:14:25.5021075Z         
2025-03-14T05:14:25.5021377Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:25.5021539Z         getitem_80: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:25.5021660Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_80 / 1.0;  getitem_80 = None
2025-03-14T05:14:25.5021726Z         
2025-03-14T05:14:25.5022031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:25.5022200Z         getitem_81: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:25.5022317Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_81 / 1.0;  getitem_81 = None
2025-03-14T05:14:25.5022385Z         
2025-03-14T05:14:25.5022684Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:25.5022831Z         getitem_82: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:25.5022992Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_82 / 1.0;  getitem_82 = None
2025-03-14T05:14:25.5023059Z         
2025-03-14T05:14:25.5023363Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:25.5023541Z         getitem_83: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:25.5023661Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_83 / 1.0;  getitem_83 = None
2025-03-14T05:14:25.5023726Z         
2025-03-14T05:14:25.5024070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:25.5024281Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:25.5024364Z         
2025-03-14T05:14:25.5025124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:25.5025342Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:25.5025419Z         
2025-03-14T05:14:25.5025829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:25.5025973Z         getitem_84: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:25.5026131Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_84;  dx_4 = getitem_84 = None
2025-03-14T05:14:25.5026288Z         getitem_85: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:25.5026442Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_85;  mul_42 = getitem_85 = None
2025-03-14T05:14:25.5026512Z         
2025-03-14T05:14:25.5026881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:25.5027015Z         getitem_86: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:25.5027146Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_86;  dy_4 = getitem_86 = None
2025-03-14T05:14:25.5027295Z         getitem_87: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:25.5027440Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_87;  mul_43 = getitem_87 = None
2025-03-14T05:14:25.5027509Z         
2025-03-14T05:14:25.5027842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:25.5027959Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:25.5028127Z         getitem_88: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:25.5028268Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_88;  exp_8 = getitem_88 = None
2025-03-14T05:14:25.5028332Z         
2025-03-14T05:14:25.5028690Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:25.5028804Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:25.5028979Z         getitem_89: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:25.5029111Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_89;  exp_9 = getitem_89 = None
2025-03-14T05:14:25.5029188Z         
2025-03-14T05:14:25.5029514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:25.5029623Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:25.5029738Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:25.5029812Z         
2025-03-14T05:14:25.5030114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:25.5030217Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:25.5030330Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:25.5030407Z         
2025-03-14T05:14:25.5030706Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:25.5030829Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:25.5030958Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:25.5031033Z         
2025-03-14T05:14:25.5031332Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:25.5031470Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:25.5031599Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:25.5031671Z         
2025-03-14T05:14:25.5032030Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:25.5032224Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:25.5032290Z         
2025-03-14T05:14:25.5032624Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:25.5032780Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:25.5032853Z         
2025-03-14T05:14:25.5033233Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:25.5033411Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:25.5033476Z         
2025-03-14T05:14:25.5033957Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:25.5034091Z         arange_10: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:25.5034164Z         
2025-03-14T05:14:25.5034458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5034628Z         batch_idx: "i64[4][1]cpu" = arange_10.to(device(type='cpu'));  arange_10 = None
2025-03-14T05:14:25.5034695Z         
2025-03-14T05:14:25.5035132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.5035248Z         topk = logits_i.topk(1000, dim = 1);  logits_i = None
2025-03-14T05:14:25.5035360Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:25.5035500Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:25.5035575Z         
2025-03-14T05:14:25.5036031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.5036175Z         getitem_92: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.5036413Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_92, topk_idx)];  proposals_i_5 = getitem_92 = topk_idx = None
2025-03-14T05:14:25.5036480Z         
2025-03-14T05:14:25.5036939Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.5037109Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.5037183Z         
2025-03-14T05:14:25.5037476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5037606Z         to_21: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:25.5037674Z         
2025-03-14T05:14:25.5038123Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.5038261Z         topk_1 = logits_i_1.topk(1000, dim = 1);  logits_i_1 = None
2025-03-14T05:14:25.5038379Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:25.5038499Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:25.5038576Z         
2025-03-14T05:14:25.5039032Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.5039170Z         getitem_96: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.5039403Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_96, topk_idx_1)];  proposals_i_6 = getitem_96 = topk_idx_1 = None
2025-03-14T05:14:25.5039476Z         
2025-03-14T05:14:25.5039929Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.5040108Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.5040172Z         
2025-03-14T05:14:25.5040476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5040600Z         to_22: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:25.5040674Z         
2025-03-14T05:14:25.5041122Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.5041245Z         topk_2 = logits_i_2.topk(1000, dim = 1);  logits_i_2 = None
2025-03-14T05:14:25.5041354Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:25.5041481Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:25.5041546Z         
2025-03-14T05:14:25.5042024Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.5042159Z         getitem_100: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.5042404Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_100, topk_idx_2)];  proposals_i_7 = getitem_100 = topk_idx_2 = None
2025-03-14T05:14:25.5042486Z         
2025-03-14T05:14:25.5042933Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.5043106Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.5043173Z         
2025-03-14T05:14:25.5043469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5043594Z         to_23: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:25.5043667Z         
2025-03-14T05:14:25.5044109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.5044234Z         topk_3 = logits_i_3.topk(1000, dim = 1);  logits_i_3 = None
2025-03-14T05:14:25.5044339Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:25.5044482Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:25.5044549Z         
2025-03-14T05:14:25.5045005Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.5045139Z         getitem_104: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:25.5045419Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_104, topk_idx_3)];  proposals_i_8 = getitem_104 = topk_idx_3 = None
2025-03-14T05:14:25.5045488Z         
2025-03-14T05:14:25.5045945Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.5046108Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.5046185Z         
2025-03-14T05:14:25.5046472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5046605Z         to_24: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:25.5046670Z         
2025-03-14T05:14:25.5047103Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:25.5047244Z         topk_4 = logits_i_4.topk(1000, dim = 1);  logits_i_4 = None
2025-03-14T05:14:25.5047356Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:25.5047473Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:25.5047549Z         
2025-03-14T05:14:25.5048011Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:25.5048188Z         getitem_108: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:25.5048416Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_108, topk_idx_4)];  proposals_i_9 = getitem_108 = topk_idx_4 = None
2025-03-14T05:14:25.5048495Z         
2025-03-14T05:14:25.5048944Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:25.5049107Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:25.5049189Z         
2025-03-14T05:14:25.5049474Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:25.5049604Z         to_25: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:25.5049669Z         
2025-03-14T05:14:25.5049950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.5050334Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:25.5050410Z         
2025-03-14T05:14:25.5050687Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.5051171Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:25.5051236Z         
2025-03-14T05:14:25.5051514Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:25.5051713Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_21, to_22, to_23, to_24, to_25], 0);  to_21 = to_22 = to_23 = to_24 = to_25 = level_ids = None
2025-03-14T05:14:25.5051790Z         
2025-03-14T05:14:25.5052177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:25.5052329Z         getitem_110: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:25.5052394Z         
2025-03-14T05:14:25.5052699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:25.5052848Z         tensor_5: "f32[5000, 4][4, 1]cpu" = getitem_110.to(torch.float32);  getitem_110 = None
2025-03-14T05:14:25.5052922Z         
2025-03-14T05:14:25.5053299Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:25.5053464Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:25.5053529Z         
2025-03-14T05:14:25.5054018Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:25.5054166Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor_5);  tensor_5 = None
2025-03-14T05:14:25.5054308Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:25.5054474Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:25.5054605Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:25.5054678Z         
2025-03-14T05:14:25.5055045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:25.5055172Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:25.5055238Z         
2025-03-14T05:14:27.3322106Z 
2025-03-14T05:14:27.3322671Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:27.3324703Z     def forward(self, L_pred_anchor_deltas_0_: "f32[4, 269952, 4][1079808, 4, 1]cpu", L_anchors_0_tensor: "f32[269952, 4][4, 1]cpu", L_pred_anchor_deltas_1_: "f32[4, 67488, 4][269952, 4, 1]cpu", L_anchors_1_tensor: "f32[67488, 4][4, 1]cpu", L_pred_anchor_deltas_2_: "f32[4, 16872, 4][67488, 4, 1]cpu", L_anchors_2_tensor: "f32[16872, 4][4, 1]cpu", L_pred_anchor_deltas_3_: "f32[4, 4218, 4][16872, 4, 1]cpu", L_anchors_3_tensor: "f32[4218, 4][4, 1]cpu", L_pred_anchor_deltas_4_: "f32[4, 1083, 4][4332, 4, 1]cpu", L_anchors_4_tensor: "f32[1083, 4][4, 1]cpu", L_pred_objectness_logits_0_: "f32[4, 269952][269952, 1]cpu", L_pred_objectness_logits_1_: "f32[4, 67488][67488, 1]cpu", L_pred_objectness_logits_2_: "f32[4, 16872][16872, 1]cpu", L_pred_objectness_logits_3_: "f32[4, 4218][4218, 1]cpu", L_pred_objectness_logits_4_: "f32[4, 1083][1083, 1]cpu"):
2025-03-14T05:14:27.3326311Z         l_pred_anchor_deltas_0_ = L_pred_anchor_deltas_0_
2025-03-14T05:14:27.3326672Z         l_anchors_0_tensor = L_anchors_0_tensor
2025-03-14T05:14:27.3326945Z         l_pred_anchor_deltas_1_ = L_pred_anchor_deltas_1_
2025-03-14T05:14:27.3327205Z         l_anchors_1_tensor = L_anchors_1_tensor
2025-03-14T05:14:27.3327462Z         l_pred_anchor_deltas_2_ = L_pred_anchor_deltas_2_
2025-03-14T05:14:27.3327711Z         l_anchors_2_tensor = L_anchors_2_tensor
2025-03-14T05:14:27.3327962Z         l_pred_anchor_deltas_3_ = L_pred_anchor_deltas_3_
2025-03-14T05:14:27.3328217Z         l_anchors_3_tensor = L_anchors_3_tensor
2025-03-14T05:14:27.3328467Z         l_pred_anchor_deltas_4_ = L_pred_anchor_deltas_4_
2025-03-14T05:14:27.3328721Z         l_anchors_4_tensor = L_anchors_4_tensor
2025-03-14T05:14:27.3329001Z         l_pred_objectness_logits_0_ = L_pred_objectness_logits_0_
2025-03-14T05:14:27.3329307Z         l_pred_objectness_logits_1_ = L_pred_objectness_logits_1_
2025-03-14T05:14:27.3329621Z         l_pred_objectness_logits_2_ = L_pred_objectness_logits_2_
2025-03-14T05:14:27.3329934Z         l_pred_objectness_logits_3_ = L_pred_objectness_logits_3_
2025-03-14T05:14:27.3330234Z         l_pred_objectness_logits_4_ = L_pred_objectness_logits_4_
2025-03-14T05:14:27.3330500Z         
2025-03-14T05:14:27.3331119Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:27.3331873Z         pred_anchor_deltas_i: "f32[1079808, 4][4, 1]cpu" = l_pred_anchor_deltas_0_.reshape(-1, 4);  l_pred_anchor_deltas_0_ = None
2025-03-14T05:14:27.3332250Z         
2025-03-14T05:14:27.3332934Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:27.3333681Z         unsqueeze: "f32[1, 269952, 4][1079808, 4, 1]cpu" = l_anchors_0_tensor.unsqueeze(0);  l_anchors_0_tensor = None
2025-03-14T05:14:27.3334120Z         expand: "f32[4, 269952, 4][0, 4, 1]cpu" = unsqueeze.expand(4, -1, -1);  unsqueeze = None
2025-03-14T05:14:27.3334487Z         anchors_i: "f32[1079808, 4][4, 1]cpu" = expand.reshape(-1, 4);  expand = None
2025-03-14T05:14:27.3334827Z         
2025-03-14T05:14:27.3335317Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:27.3335913Z         deltas: "f32[1079808, 4][4, 1]cpu" = pred_anchor_deltas_i.float();  pred_anchor_deltas_i = None
2025-03-14T05:14:27.3336206Z         
2025-03-14T05:14:27.3336609Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:27.3337124Z         boxes: "f32[1079808, 4][4, 1]cpu" = anchors_i.to(torch.float32);  anchors_i = None
2025-03-14T05:14:27.3337394Z         
2025-03-14T05:14:27.3337799Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:27.3338300Z         getitem: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:27.3338615Z         getitem_1: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3338941Z         widths: "f32[1079808][1]cpu" = getitem - getitem_1;  getitem = getitem_1 = None
2025-03-14T05:14:27.3339206Z         
2025-03-14T05:14:27.3339694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:27.3340202Z         getitem_2: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:27.3340515Z         getitem_3: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:27.3340864Z         heights: "f32[1079808][1]cpu" = getitem_2 - getitem_3;  getitem_2 = getitem_3 = None
2025-03-14T05:14:27.3341140Z         
2025-03-14T05:14:27.3341555Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:27.3342068Z         getitem_4: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3342355Z         mul: "f32[1079808][1]cpu" = 0.5 * widths
2025-03-14T05:14:27.3342631Z         ctr_x: "f32[1079808][1]cpu" = getitem_4 + mul;  getitem_4 = mul = None
2025-03-14T05:14:27.3342953Z         
2025-03-14T05:14:27.3343372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:27.3343902Z         getitem_5: "f32[1079808][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:27.3344276Z         mul_1: "f32[1079808][1]cpu" = 0.5 * heights
2025-03-14T05:14:27.3344576Z         ctr_y: "f32[1079808][1]cpu" = getitem_5 + mul_1;  getitem_5 = mul_1 = None
2025-03-14T05:14:27.3344838Z         
2025-03-14T05:14:27.3345286Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:27.3345830Z         getitem_6: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:27.3346175Z         dx: "f32[1079808, 1][1, 1]cpu" = getitem_6 / 1.0;  getitem_6 = None
2025-03-14T05:14:27.3346420Z         
2025-03-14T05:14:27.3346853Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:27.3347382Z         getitem_7: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:27.3347712Z         dy: "f32[1079808, 1][1, 1]cpu" = getitem_7 / 1.0;  getitem_7 = None
2025-03-14T05:14:27.3347948Z         
2025-03-14T05:14:27.3348340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:27.3348872Z         getitem_8: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:27.3349198Z         dw: "f32[1079808, 1][1, 1]cpu" = getitem_8 / 1.0;  getitem_8 = None
2025-03-14T05:14:27.3349434Z         
2025-03-14T05:14:27.3349824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:27.3350368Z         getitem_9: "f32[1079808, 1][4, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:27.3350715Z         dh: "f32[1079808, 1][1, 1]cpu" = getitem_9 / 1.0;  getitem_9 = None
2025-03-14T05:14:27.3350952Z         
2025-03-14T05:14:27.3351382Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:27.3351922Z         dw_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:27.3352186Z         
2025-03-14T05:14:27.3352606Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:27.3353137Z         dh_1: "f32[1079808, 1][1, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:27.3353402Z         
2025-03-14T05:14:27.3353854Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:27.3354427Z         getitem_10: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:27.3354753Z         mul_2: "f32[1079808, 1][1, 1]cpu" = dx * getitem_10;  dx = getitem_10 = None
2025-03-14T05:14:27.3355095Z         getitem_11: "f32[1079808, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:27.3355453Z         pred_ctr_x: "f32[1079808, 1][1, 1]cpu" = mul_2 + getitem_11;  mul_2 = getitem_11 = None
2025-03-14T05:14:27.3355716Z         
2025-03-14T05:14:27.3356150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:27.3356698Z         getitem_12: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:27.3357023Z         mul_3: "f32[1079808, 1][1, 1]cpu" = dy * getitem_12;  dy = getitem_12 = None
2025-03-14T05:14:27.3357360Z         getitem_13: "f32[1079808, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:27.3357711Z         pred_ctr_y: "f32[1079808, 1][1, 1]cpu" = mul_3 + getitem_13;  mul_3 = getitem_13 = None
2025-03-14T05:14:27.3357976Z         
2025-03-14T05:14:27.3358399Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:27.3358905Z         exp: "f32[1079808, 1][1, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:27.3359244Z         getitem_14: "f32[1079808, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:27.3359598Z         pred_w: "f32[1079808, 1][1, 1]cpu" = exp * getitem_14;  exp = getitem_14 = None
2025-03-14T05:14:27.3359878Z         
2025-03-14T05:14:27.3360315Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:27.3360839Z         exp_1: "f32[1079808, 1][1, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:27.3361188Z         getitem_15: "f32[1079808, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:27.3361577Z         pred_h: "f32[1079808, 1][1, 1]cpu" = exp_1 * getitem_15;  exp_1 = getitem_15 = None
2025-03-14T05:14:27.3361839Z         
2025-03-14T05:14:27.3362234Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:27.3362701Z         mul_6: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:27.3362972Z         x1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:27.3363222Z         
2025-03-14T05:14:27.3363631Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:27.3364103Z         mul_7: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:27.3364388Z         y1: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:27.3364623Z         
2025-03-14T05:14:27.3365021Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:27.3365510Z         mul_8: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:27.3365818Z         x2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:27.3366077Z         
2025-03-14T05:14:27.3366493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:27.3366982Z         mul_9: "f32[1079808, 1][1, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:27.3367283Z         y2: "f32[1079808, 1][1, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:27.3367592Z         
2025-03-14T05:14:27.3368043Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:27.3368644Z         pred_boxes: "f32[1079808, 1, 4][4, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:27.3368948Z         
2025-03-14T05:14:27.3369387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:27.3369963Z         proposals_i: "f32[1079808, 4][4, 1]cpu" = pred_boxes.reshape((1079808, 4));  pred_boxes = None
2025-03-14T05:14:27.3370261Z         
2025-03-14T05:14:27.3370757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:27.3371394Z         proposals_i_5: "f32[4, 269952, 4][1079808, 4, 1]cpu" = proposals_i.view(4, -1, 4);  proposals_i = None
2025-03-14T05:14:27.3371704Z         
2025-03-14T05:14:27.3372207Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:27.3372903Z         pred_anchor_deltas_i_1: "f32[269952, 4][4, 1]cpu" = l_pred_anchor_deltas_1_.reshape(-1, 4);  l_pred_anchor_deltas_1_ = None
2025-03-14T05:14:27.3373240Z         
2025-03-14T05:14:27.3373777Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:27.3374483Z         unsqueeze_1: "f32[1, 67488, 4][269952, 4, 1]cpu" = l_anchors_1_tensor.unsqueeze(0);  l_anchors_1_tensor = None
2025-03-14T05:14:27.3374897Z         expand_1: "f32[4, 67488, 4][0, 4, 1]cpu" = unsqueeze_1.expand(4, -1, -1);  unsqueeze_1 = None
2025-03-14T05:14:27.3375258Z         anchors_i_1: "f32[269952, 4][4, 1]cpu" = expand_1.reshape(-1, 4);  expand_1 = None
2025-03-14T05:14:27.3375533Z         
2025-03-14T05:14:27.3376027Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:27.3376647Z         deltas_1: "f32[269952, 4][4, 1]cpu" = pred_anchor_deltas_i_1.float();  pred_anchor_deltas_i_1 = None
2025-03-14T05:14:27.3376940Z         
2025-03-14T05:14:27.3377338Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:27.3377848Z         boxes_1: "f32[269952, 4][4, 1]cpu" = anchors_i_1.to(torch.float32);  anchors_i_1 = None
2025-03-14T05:14:27.3378119Z         
2025-03-14T05:14:27.3378530Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:27.3379034Z         getitem_16: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 2)]
2025-03-14T05:14:27.3379355Z         getitem_17: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3379695Z         widths_1: "f32[269952][1]cpu" = getitem_16 - getitem_17;  getitem_16 = getitem_17 = None
2025-03-14T05:14:27.3379973Z         
2025-03-14T05:14:27.3380394Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:27.3380897Z         getitem_18: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 3)]
2025-03-14T05:14:27.3381208Z         getitem_19: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)]
2025-03-14T05:14:27.3382050Z         heights_1: "f32[269952][1]cpu" = getitem_18 - getitem_19;  getitem_18 = getitem_19 = None
2025-03-14T05:14:27.3382336Z         
2025-03-14T05:14:27.3382746Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:27.3383238Z         getitem_20: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3383515Z         mul_10: "f32[269952][1]cpu" = 0.5 * widths_1
2025-03-14T05:14:27.3383805Z         ctr_x_1: "f32[269952][1]cpu" = getitem_20 + mul_10;  getitem_20 = mul_10 = None
2025-03-14T05:14:27.3384068Z         
2025-03-14T05:14:27.3384544Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:27.3385084Z         getitem_21: "f32[269952][4]cpu" = boxes_1[(slice(None, None, None), 1)];  boxes_1 = None
2025-03-14T05:14:27.3385384Z         mul_11: "f32[269952][1]cpu" = 0.5 * heights_1
2025-03-14T05:14:27.3385668Z         ctr_y_1: "f32[269952][1]cpu" = getitem_21 + mul_11;  getitem_21 = mul_11 = None
2025-03-14T05:14:27.3385926Z         
2025-03-14T05:14:27.3386337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:27.3386868Z         getitem_22: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:27.3387204Z         dx_1: "f32[269952, 1][1, 1]cpu" = getitem_22 / 1.0;  getitem_22 = None
2025-03-14T05:14:27.3387523Z         
2025-03-14T05:14:27.3387930Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:27.3388461Z         getitem_23: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:27.3388803Z         dy_1: "f32[269952, 1][1, 1]cpu" = getitem_23 / 1.0;  getitem_23 = None
2025-03-14T05:14:27.3389053Z         
2025-03-14T05:14:27.3389501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:27.3390033Z         getitem_24: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:27.3390370Z         dw_2: "f32[269952, 1][1, 1]cpu" = getitem_24 / 1.0;  getitem_24 = None
2025-03-14T05:14:27.3390607Z         
2025-03-14T05:14:27.3391000Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:27.3391552Z         getitem_25: "f32[269952, 1][4, 4]cpu" = deltas_1[(slice(None, None, None), slice(3, None, 4))];  deltas_1 = None
2025-03-14T05:14:27.3391916Z         dh_2: "f32[269952, 1][1, 1]cpu" = getitem_25 / 1.0;  getitem_25 = None
2025-03-14T05:14:27.3392165Z         
2025-03-14T05:14:27.3392605Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:27.3393157Z         dw_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dw_2, max = 4.135166556742356);  dw_2 = None
2025-03-14T05:14:27.3393434Z         
2025-03-14T05:14:27.3393857Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:27.3394400Z         dh_3: "f32[269952, 1][1, 1]cpu" = torch.clamp(dh_2, max = 4.135166556742356);  dh_2 = None
2025-03-14T05:14:27.3394705Z         
2025-03-14T05:14:27.3395154Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:27.3395736Z         getitem_26: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)]
2025-03-14T05:14:27.3396072Z         mul_12: "f32[269952, 1][1, 1]cpu" = dx_1 * getitem_26;  dx_1 = getitem_26 = None
2025-03-14T05:14:27.3396424Z         getitem_27: "f32[269952, 1][1, 1]cpu" = ctr_x_1[(slice(None, None, None), None)];  ctr_x_1 = None
2025-03-14T05:14:27.3396795Z         pred_ctr_x_1: "f32[269952, 1][1, 1]cpu" = mul_12 + getitem_27;  mul_12 = getitem_27 = None
2025-03-14T05:14:27.3397069Z         
2025-03-14T05:14:27.3397512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:27.3398071Z         getitem_28: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)]
2025-03-14T05:14:27.3398403Z         mul_13: "f32[269952, 1][1, 1]cpu" = dy_1 * getitem_28;  dy_1 = getitem_28 = None
2025-03-14T05:14:27.3398750Z         getitem_29: "f32[269952, 1][1, 1]cpu" = ctr_y_1[(slice(None, None, None), None)];  ctr_y_1 = None
2025-03-14T05:14:27.3399112Z         pred_ctr_y_1: "f32[269952, 1][1, 1]cpu" = mul_13 + getitem_29;  mul_13 = getitem_29 = None
2025-03-14T05:14:27.3399389Z         
2025-03-14T05:14:27.3399819Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:27.3400331Z         exp_2: "f32[269952, 1][1, 1]cpu" = torch.exp(dw_3);  dw_3 = None
2025-03-14T05:14:27.3400675Z         getitem_30: "f32[269952, 1][1, 1]cpu" = widths_1[(slice(None, None, None), None)];  widths_1 = None
2025-03-14T05:14:27.3401076Z         pred_w_1: "f32[269952, 1][1, 1]cpu" = exp_2 * getitem_30;  exp_2 = getitem_30 = None
2025-03-14T05:14:27.3401343Z         
2025-03-14T05:14:27.3401772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:27.3402293Z         exp_3: "f32[269952, 1][1, 1]cpu" = torch.exp(dh_3);  dh_3 = None
2025-03-14T05:14:27.3402641Z         getitem_31: "f32[269952, 1][1, 1]cpu" = heights_1[(slice(None, None, None), None)];  heights_1 = None
2025-03-14T05:14:27.3403027Z         pred_h_1: "f32[269952, 1][1, 1]cpu" = exp_3 * getitem_31;  exp_3 = getitem_31 = None
2025-03-14T05:14:27.3403292Z         
2025-03-14T05:14:27.3403703Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:27.3404180Z         mul_16: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1
2025-03-14T05:14:27.3404463Z         x1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 - mul_16;  mul_16 = None
2025-03-14T05:14:27.3404711Z         
2025-03-14T05:14:27.3405116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:27.3405602Z         mul_17: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1
2025-03-14T05:14:27.3405860Z         y1_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 - mul_17;  mul_17 = None
2025-03-14T05:14:27.3406100Z         
2025-03-14T05:14:27.3406520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:27.3407006Z         mul_18: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_w_1;  pred_w_1 = None
2025-03-14T05:14:27.3407316Z         x2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_x_1 + mul_18;  pred_ctr_x_1 = mul_18 = None
2025-03-14T05:14:27.3407574Z         
2025-03-14T05:14:27.3407984Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:27.3408459Z         mul_19: "f32[269952, 1][1, 1]cpu" = 0.5 * pred_h_1;  pred_h_1 = None
2025-03-14T05:14:27.3408788Z         y2_1: "f32[269952, 1][1, 1]cpu" = pred_ctr_y_1 + mul_19;  pred_ctr_y_1 = mul_19 = None
2025-03-14T05:14:27.3409048Z         
2025-03-14T05:14:27.3409483Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:27.3410078Z         pred_boxes_1: "f32[269952, 1, 4][4, 4, 1]cpu" = torch.stack((x1_1, y1_1, x2_1, y2_1), dim = -1);  x1_1 = y1_1 = x2_1 = y2_1 = None
2025-03-14T05:14:27.3410382Z         
2025-03-14T05:14:27.3410810Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:27.3411361Z         proposals_i_1: "f32[269952, 4][4, 1]cpu" = pred_boxes_1.reshape((269952, 4));  pred_boxes_1 = None
2025-03-14T05:14:27.3411653Z         
2025-03-14T05:14:27.3412124Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:27.3412736Z         proposals_i_6: "f32[4, 67488, 4][269952, 4, 1]cpu" = proposals_i_1.view(4, -1, 4);  proposals_i_1 = None
2025-03-14T05:14:27.3413031Z         
2025-03-14T05:14:27.3413512Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:27.3414171Z         pred_anchor_deltas_i_2: "f32[67488, 4][4, 1]cpu" = l_pred_anchor_deltas_2_.reshape(-1, 4);  l_pred_anchor_deltas_2_ = None
2025-03-14T05:14:27.3414531Z         
2025-03-14T05:14:27.3415054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:27.3415731Z         unsqueeze_2: "f32[1, 16872, 4][67488, 4, 1]cpu" = l_anchors_2_tensor.unsqueeze(0);  l_anchors_2_tensor = None
2025-03-14T05:14:27.3416128Z         expand_2: "f32[4, 16872, 4][0, 4, 1]cpu" = unsqueeze_2.expand(4, -1, -1);  unsqueeze_2 = None
2025-03-14T05:14:27.3416497Z         anchors_i_2: "f32[67488, 4][4, 1]cpu" = expand_2.reshape(-1, 4);  expand_2 = None
2025-03-14T05:14:27.3416765Z         
2025-03-14T05:14:27.3417222Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:27.3417812Z         deltas_2: "f32[67488, 4][4, 1]cpu" = pred_anchor_deltas_i_2.float();  pred_anchor_deltas_i_2 = None
2025-03-14T05:14:27.3418105Z         
2025-03-14T05:14:27.3418503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:27.3419011Z         boxes_2: "f32[67488, 4][4, 1]cpu" = anchors_i_2.to(torch.float32);  anchors_i_2 = None
2025-03-14T05:14:27.3419280Z         
2025-03-14T05:14:27.3419683Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:27.3420181Z         getitem_32: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 2)]
2025-03-14T05:14:27.3420490Z         getitem_33: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3420823Z         widths_2: "f32[67488][1]cpu" = getitem_32 - getitem_33;  getitem_32 = getitem_33 = None
2025-03-14T05:14:27.3421096Z         
2025-03-14T05:14:27.3421520Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:27.3422019Z         getitem_34: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 3)]
2025-03-14T05:14:27.3422352Z         getitem_35: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)]
2025-03-14T05:14:27.3422693Z         heights_2: "f32[67488][1]cpu" = getitem_34 - getitem_35;  getitem_34 = getitem_35 = None
2025-03-14T05:14:27.3422973Z         
2025-03-14T05:14:27.3423385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:27.3423892Z         getitem_36: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3424256Z         mul_20: "f32[67488][1]cpu" = 0.5 * widths_2
2025-03-14T05:14:27.3424564Z         ctr_x_2: "f32[67488][1]cpu" = getitem_36 + mul_20;  getitem_36 = mul_20 = None
2025-03-14T05:14:27.3424824Z         
2025-03-14T05:14:27.3425240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:27.3425776Z         getitem_37: "f32[67488][4]cpu" = boxes_2[(slice(None, None, None), 1)];  boxes_2 = None
2025-03-14T05:14:27.3426085Z         mul_21: "f32[67488][1]cpu" = 0.5 * heights_2
2025-03-14T05:14:27.3426364Z         ctr_y_2: "f32[67488][1]cpu" = getitem_37 + mul_21;  getitem_37 = mul_21 = None
2025-03-14T05:14:27.3426623Z         
2025-03-14T05:14:27.3427029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:27.3427560Z         getitem_38: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:27.3428618Z         dx_2: "f32[67488, 1][1, 1]cpu" = getitem_38 / 1.0;  getitem_38 = None
2025-03-14T05:14:27.3428865Z         
2025-03-14T05:14:27.3429265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:27.3429786Z         getitem_39: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:27.3430119Z         dy_2: "f32[67488, 1][1, 1]cpu" = getitem_39 / 1.0;  getitem_39 = None
2025-03-14T05:14:27.3430361Z         
2025-03-14T05:14:27.3430788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:27.3431309Z         getitem_40: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:27.3431642Z         dw_4: "f32[67488, 1][1, 1]cpu" = getitem_40 / 1.0;  getitem_40 = None
2025-03-14T05:14:27.3431888Z         
2025-03-14T05:14:27.3432290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:27.3432851Z         getitem_41: "f32[67488, 1][4, 4]cpu" = deltas_2[(slice(None, None, None), slice(3, None, 4))];  deltas_2 = None
2025-03-14T05:14:27.3433213Z         dh_4: "f32[67488, 1][1, 1]cpu" = getitem_41 / 1.0;  getitem_41 = None
2025-03-14T05:14:27.3433460Z         
2025-03-14T05:14:27.3433897Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:27.3434448Z         dw_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dw_4, max = 4.135166556742356);  dw_4 = None
2025-03-14T05:14:27.3434720Z         
2025-03-14T05:14:27.3435151Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:27.3435729Z         dh_5: "f32[67488, 1][1, 1]cpu" = torch.clamp(dh_4, max = 4.135166556742356);  dh_4 = None
2025-03-14T05:14:27.3436000Z         
2025-03-14T05:14:27.3436453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:27.3437003Z         getitem_42: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)]
2025-03-14T05:14:27.3437353Z         mul_22: "f32[67488, 1][1, 1]cpu" = dx_2 * getitem_42;  dx_2 = getitem_42 = None
2025-03-14T05:14:27.3437690Z         getitem_43: "f32[67488, 1][1, 1]cpu" = ctr_x_2[(slice(None, None, None), None)];  ctr_x_2 = None
2025-03-14T05:14:27.3438041Z         pred_ctr_x_2: "f32[67488, 1][1, 1]cpu" = mul_22 + getitem_43;  mul_22 = getitem_43 = None
2025-03-14T05:14:27.3438304Z         
2025-03-14T05:14:27.3438739Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:27.3439276Z         getitem_44: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)]
2025-03-14T05:14:27.3439594Z         mul_23: "f32[67488, 1][1, 1]cpu" = dy_2 * getitem_44;  dy_2 = getitem_44 = None
2025-03-14T05:14:27.3439927Z         getitem_45: "f32[67488, 1][1, 1]cpu" = ctr_y_2[(slice(None, None, None), None)];  ctr_y_2 = None
2025-03-14T05:14:27.3440282Z         pred_ctr_y_2: "f32[67488, 1][1, 1]cpu" = mul_23 + getitem_45;  mul_23 = getitem_45 = None
2025-03-14T05:14:27.3440550Z         
2025-03-14T05:14:27.3440974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:27.3441475Z         exp_4: "f32[67488, 1][1, 1]cpu" = torch.exp(dw_5);  dw_5 = None
2025-03-14T05:14:27.3441856Z         getitem_46: "f32[67488, 1][1, 1]cpu" = widths_2[(slice(None, None, None), None)];  widths_2 = None
2025-03-14T05:14:27.3442213Z         pred_w_2: "f32[67488, 1][1, 1]cpu" = exp_4 * getitem_46;  exp_4 = getitem_46 = None
2025-03-14T05:14:27.3442472Z         
2025-03-14T05:14:27.3442893Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:27.3443389Z         exp_5: "f32[67488, 1][1, 1]cpu" = torch.exp(dh_5);  dh_5 = None
2025-03-14T05:14:27.3443746Z         getitem_47: "f32[67488, 1][1, 1]cpu" = heights_2[(slice(None, None, None), None)];  heights_2 = None
2025-03-14T05:14:27.3444105Z         pred_h_2: "f32[67488, 1][1, 1]cpu" = exp_5 * getitem_47;  exp_5 = getitem_47 = None
2025-03-14T05:14:27.3444364Z         
2025-03-14T05:14:27.3444765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:27.3445234Z         mul_26: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2
2025-03-14T05:14:27.3445504Z         x1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 - mul_26;  mul_26 = None
2025-03-14T05:14:27.3445743Z         
2025-03-14T05:14:27.3446141Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:27.3446602Z         mul_27: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2
2025-03-14T05:14:27.3446862Z         y1_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 - mul_27;  mul_27 = None
2025-03-14T05:14:27.3447098Z         
2025-03-14T05:14:27.3447493Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:27.3447978Z         mul_28: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_w_2;  pred_w_2 = None
2025-03-14T05:14:27.3448315Z         x2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_x_2 + mul_28;  pred_ctr_x_2 = mul_28 = None
2025-03-14T05:14:27.3448577Z         
2025-03-14T05:14:27.3448971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:27.3449474Z         mul_29: "f32[67488, 1][1, 1]cpu" = 0.5 * pred_h_2;  pred_h_2 = None
2025-03-14T05:14:27.3449777Z         y2_2: "f32[67488, 1][1, 1]cpu" = pred_ctr_y_2 + mul_29;  pred_ctr_y_2 = mul_29 = None
2025-03-14T05:14:27.3450032Z         
2025-03-14T05:14:27.3450470Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:27.3451061Z         pred_boxes_2: "f32[67488, 1, 4][4, 4, 1]cpu" = torch.stack((x1_2, y1_2, x2_2, y2_2), dim = -1);  x1_2 = y1_2 = x2_2 = y2_2 = None
2025-03-14T05:14:27.3451364Z         
2025-03-14T05:14:27.3451788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:27.3452338Z         proposals_i_2: "f32[67488, 4][4, 1]cpu" = pred_boxes_2.reshape((67488, 4));  pred_boxes_2 = None
2025-03-14T05:14:27.3452625Z         
2025-03-14T05:14:27.3453084Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:27.3453686Z         proposals_i_7: "f32[4, 16872, 4][67488, 4, 1]cpu" = proposals_i_2.view(4, -1, 4);  proposals_i_2 = None
2025-03-14T05:14:27.3453982Z         
2025-03-14T05:14:27.3454459Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:27.3455141Z         pred_anchor_deltas_i_3: "f32[16872, 4][4, 1]cpu" = l_pred_anchor_deltas_3_.reshape(-1, 4);  l_pred_anchor_deltas_3_ = None
2025-03-14T05:14:27.3455468Z         
2025-03-14T05:14:27.3455986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:27.3456650Z         unsqueeze_3: "f32[1, 4218, 4][16872, 4, 1]cpu" = l_anchors_3_tensor.unsqueeze(0);  l_anchors_3_tensor = None
2025-03-14T05:14:27.3457054Z         expand_3: "f32[4, 4218, 4][0, 4, 1]cpu" = unsqueeze_3.expand(4, -1, -1);  unsqueeze_3 = None
2025-03-14T05:14:27.3457401Z         anchors_i_3: "f32[16872, 4][4, 1]cpu" = expand_3.reshape(-1, 4);  expand_3 = None
2025-03-14T05:14:27.3457660Z         
2025-03-14T05:14:27.3458116Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:27.3458703Z         deltas_3: "f32[16872, 4][4, 1]cpu" = pred_anchor_deltas_i_3.float();  pred_anchor_deltas_i_3 = None
2025-03-14T05:14:27.3458993Z         
2025-03-14T05:14:27.3459385Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:27.3459894Z         boxes_3: "f32[16872, 4][4, 1]cpu" = anchors_i_3.to(torch.float32);  anchors_i_3 = None
2025-03-14T05:14:27.3460160Z         
2025-03-14T05:14:27.3460557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:27.3461053Z         getitem_48: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 2)]
2025-03-14T05:14:27.3461366Z         getitem_49: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3461719Z         widths_3: "f32[16872][1]cpu" = getitem_48 - getitem_49;  getitem_48 = getitem_49 = None
2025-03-14T05:14:27.3461993Z         
2025-03-14T05:14:27.3462392Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:27.3462904Z         getitem_50: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 3)]
2025-03-14T05:14:27.3463209Z         getitem_51: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)]
2025-03-14T05:14:27.3463545Z         heights_3: "f32[16872][1]cpu" = getitem_50 - getitem_51;  getitem_50 = getitem_51 = None
2025-03-14T05:14:27.3463826Z         
2025-03-14T05:14:27.3505230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:27.3506215Z         getitem_52: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3506621Z         mul_30: "f32[16872][1]cpu" = 0.5 * widths_3
2025-03-14T05:14:27.3506929Z         ctr_x_3: "f32[16872][1]cpu" = getitem_52 + mul_30;  getitem_52 = mul_30 = None
2025-03-14T05:14:27.3507260Z         
2025-03-14T05:14:27.3507757Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:27.3508397Z         getitem_53: "f32[16872][4]cpu" = boxes_3[(slice(None, None, None), 1)];  boxes_3 = None
2025-03-14T05:14:27.3508719Z         mul_31: "f32[16872][1]cpu" = 0.5 * heights_3
2025-03-14T05:14:27.3509012Z         ctr_y_3: "f32[16872][1]cpu" = getitem_53 + mul_31;  getitem_53 = mul_31 = None
2025-03-14T05:14:27.3509292Z         
2025-03-14T05:14:27.3509733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:27.3510598Z         getitem_54: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:27.3510943Z         dx_3: "f32[16872, 1][1, 1]cpu" = getitem_54 / 1.0;  getitem_54 = None
2025-03-14T05:14:27.3511193Z         
2025-03-14T05:14:27.3511594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:27.3512113Z         getitem_55: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:27.3512497Z         dy_3: "f32[16872, 1][1, 1]cpu" = getitem_55 / 1.0;  getitem_55 = None
2025-03-14T05:14:27.3512739Z         
2025-03-14T05:14:27.3513128Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:27.3513638Z         getitem_56: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:27.3513960Z         dw_6: "f32[16872, 1][1, 1]cpu" = getitem_56 / 1.0;  getitem_56 = None
2025-03-14T05:14:27.3514198Z         
2025-03-14T05:14:27.3514593Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:27.3515137Z         getitem_57: "f32[16872, 1][4, 4]cpu" = deltas_3[(slice(None, None, None), slice(3, None, 4))];  deltas_3 = None
2025-03-14T05:14:27.3515489Z         dh_6: "f32[16872, 1][1, 1]cpu" = getitem_57 / 1.0;  getitem_57 = None
2025-03-14T05:14:27.3515727Z         
2025-03-14T05:14:27.3516161Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:27.3516708Z         dw_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dw_6, max = 4.135166556742356);  dw_6 = None
2025-03-14T05:14:27.3516976Z         
2025-03-14T05:14:27.3517448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:27.3517990Z         dh_7: "f32[16872, 1][1, 1]cpu" = torch.clamp(dh_6, max = 4.135166556742356);  dh_6 = None
2025-03-14T05:14:27.3518299Z         
2025-03-14T05:14:27.3518734Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:27.3519277Z         getitem_58: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)]
2025-03-14T05:14:27.3519601Z         mul_32: "f32[16872, 1][1, 1]cpu" = dx_3 * getitem_58;  dx_3 = getitem_58 = None
2025-03-14T05:14:27.3519937Z         getitem_59: "f32[16872, 1][1, 1]cpu" = ctr_x_3[(slice(None, None, None), None)];  ctr_x_3 = None
2025-03-14T05:14:27.3520289Z         pred_ctr_x_3: "f32[16872, 1][1, 1]cpu" = mul_32 + getitem_59;  mul_32 = getitem_59 = None
2025-03-14T05:14:27.3520557Z         
2025-03-14T05:14:27.3520993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:27.3521532Z         getitem_60: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)]
2025-03-14T05:14:27.3521851Z         mul_33: "f32[16872, 1][1, 1]cpu" = dy_3 * getitem_60;  dy_3 = getitem_60 = None
2025-03-14T05:14:27.3522183Z         getitem_61: "f32[16872, 1][1, 1]cpu" = ctr_y_3[(slice(None, None, None), None)];  ctr_y_3 = None
2025-03-14T05:14:27.3522534Z         pred_ctr_y_3: "f32[16872, 1][1, 1]cpu" = mul_33 + getitem_61;  mul_33 = getitem_61 = None
2025-03-14T05:14:27.3522801Z         
2025-03-14T05:14:27.3523220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:27.3523740Z         exp_6: "f32[16872, 1][1, 1]cpu" = torch.exp(dw_7);  dw_7 = None
2025-03-14T05:14:27.3524077Z         getitem_62: "f32[16872, 1][1, 1]cpu" = widths_3[(slice(None, None, None), None)];  widths_3 = None
2025-03-14T05:14:27.3524440Z         pred_w_3: "f32[16872, 1][1, 1]cpu" = exp_6 * getitem_62;  exp_6 = getitem_62 = None
2025-03-14T05:14:27.3524707Z         
2025-03-14T05:14:27.3525134Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:27.3525659Z         exp_7: "f32[16872, 1][1, 1]cpu" = torch.exp(dh_7);  dh_7 = None
2025-03-14T05:14:27.3525998Z         getitem_63: "f32[16872, 1][1, 1]cpu" = heights_3[(slice(None, None, None), None)];  heights_3 = None
2025-03-14T05:14:27.3526358Z         pred_h_3: "f32[16872, 1][1, 1]cpu" = exp_7 * getitem_63;  exp_7 = getitem_63 = None
2025-03-14T05:14:27.3526625Z         
2025-03-14T05:14:27.3527049Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:27.3527525Z         mul_36: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3
2025-03-14T05:14:27.3527806Z         x1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 - mul_36;  mul_36 = None
2025-03-14T05:14:27.3528059Z         
2025-03-14T05:14:27.3528454Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:27.3528926Z         mul_37: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3
2025-03-14T05:14:27.3529198Z         y1_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 - mul_37;  mul_37 = None
2025-03-14T05:14:27.3529443Z         
2025-03-14T05:14:27.3529842Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:27.3530333Z         mul_38: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_w_3;  pred_w_3 = None
2025-03-14T05:14:27.3530663Z         x2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_x_3 + mul_38;  pred_ctr_x_3 = mul_38 = None
2025-03-14T05:14:27.3530921Z         
2025-03-14T05:14:27.3531312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:27.3531831Z         mul_39: "f32[16872, 1][1, 1]cpu" = 0.5 * pred_h_3;  pred_h_3 = None
2025-03-14T05:14:27.3532135Z         y2_3: "f32[16872, 1][1, 1]cpu" = pred_ctr_y_3 + mul_39;  pred_ctr_y_3 = mul_39 = None
2025-03-14T05:14:27.3532389Z         
2025-03-14T05:14:27.3532829Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:27.3533426Z         pred_boxes_3: "f32[16872, 1, 4][4, 4, 1]cpu" = torch.stack((x1_3, y1_3, x2_3, y2_3), dim = -1);  x1_3 = y1_3 = x2_3 = y2_3 = None
2025-03-14T05:14:27.3533733Z         
2025-03-14T05:14:27.3534156Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:27.3534714Z         proposals_i_3: "f32[16872, 4][4, 1]cpu" = pred_boxes_3.reshape((16872, 4));  pred_boxes_3 = None
2025-03-14T05:14:27.3535005Z         
2025-03-14T05:14:27.3535491Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:27.3536157Z         proposals_i_8: "f32[4, 4218, 4][16872, 4, 1]cpu" = proposals_i_3.view(4, -1, 4);  proposals_i_3 = None
2025-03-14T05:14:27.3536462Z         
2025-03-14T05:14:27.3536943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:527 in _decode_proposals, code: pred_anchor_deltas_i = pred_anchor_deltas_i.reshape(-1, B)
2025-03-14T05:14:27.3537651Z         pred_anchor_deltas_i_4: "f32[4332, 4][4, 1]cpu" = l_pred_anchor_deltas_4_.reshape(-1, 4);  l_pred_anchor_deltas_4_ = None
2025-03-14T05:14:27.3537985Z         
2025-03-14T05:14:27.3538518Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:529 in _decode_proposals, code: anchors_i = anchors_i.tensor.unsqueeze(0).expand(N, -1, -1).reshape(-1, B)
2025-03-14T05:14:27.3539225Z         unsqueeze_4: "f32[1, 1083, 4][4332, 4, 1]cpu" = l_anchors_4_tensor.unsqueeze(0);  l_anchors_4_tensor = None
2025-03-14T05:14:27.3539631Z         expand_4: "f32[4, 1083, 4][0, 4, 1]cpu" = unsqueeze_4.expand(4, -1, -1);  unsqueeze_4 = None
2025-03-14T05:14:27.3539981Z         anchors_i_4: "f32[4332, 4][4, 1]cpu" = expand_4.reshape(-1, 4);  expand_4 = None
2025-03-14T05:14:27.3540247Z         
2025-03-14T05:14:27.3540723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:27.3541329Z         deltas_4: "f32[4332, 4][4, 1]cpu" = pred_anchor_deltas_i_4.float();  pred_anchor_deltas_i_4 = None
2025-03-14T05:14:27.3541625Z         
2025-03-14T05:14:27.3542041Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:27.3542685Z         boxes_4: "f32[4332, 4][4, 1]cpu" = anchors_i_4.to(torch.float32);  anchors_i_4 = None
2025-03-14T05:14:27.3542952Z         
2025-03-14T05:14:27.3543364Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:27.3543883Z         getitem_64: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 2)]
2025-03-14T05:14:27.3544293Z         getitem_65: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3544671Z         widths_4: "f32[4332][1]cpu" = getitem_64 - getitem_65;  getitem_64 = getitem_65 = None
2025-03-14T05:14:27.3544954Z         
2025-03-14T05:14:27.3545376Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:27.3545911Z         getitem_66: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 3)]
2025-03-14T05:14:27.3546222Z         getitem_67: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)]
2025-03-14T05:14:27.3546565Z         heights_4: "f32[4332][1]cpu" = getitem_66 - getitem_67;  getitem_66 = getitem_67 = None
2025-03-14T05:14:27.3546874Z         
2025-03-14T05:14:27.3547283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:27.3547782Z         getitem_68: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 0)]
2025-03-14T05:14:27.3548059Z         mul_40: "f32[4332][1]cpu" = 0.5 * widths_4
2025-03-14T05:14:27.3548344Z         ctr_x_4: "f32[4332][1]cpu" = getitem_68 + mul_40;  getitem_68 = mul_40 = None
2025-03-14T05:14:27.3548603Z         
2025-03-14T05:14:27.3549012Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:27.3549547Z         getitem_69: "f32[4332][4]cpu" = boxes_4[(slice(None, None, None), 1)];  boxes_4 = None
2025-03-14T05:14:27.3549842Z         mul_41: "f32[4332][1]cpu" = 0.5 * heights_4
2025-03-14T05:14:27.3550118Z         ctr_y_4: "f32[4332][1]cpu" = getitem_69 + mul_41;  getitem_69 = mul_41 = None
2025-03-14T05:14:27.3550370Z         
2025-03-14T05:14:27.3550770Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:27.3551302Z         getitem_70: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:27.3551624Z         dx_4: "f32[4332, 1][1, 1]cpu" = getitem_70 / 1.0;  getitem_70 = None
2025-03-14T05:14:27.3551862Z         
2025-03-14T05:14:27.3552249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:27.3552756Z         getitem_71: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:27.3553092Z         dy_4: "f32[4332, 1][1, 1]cpu" = getitem_71 / 1.0;  getitem_71 = None
2025-03-14T05:14:27.3553326Z         
2025-03-14T05:14:27.3553711Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:27.3554212Z         getitem_72: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:27.3554532Z         dw_8: "f32[4332, 1][1, 1]cpu" = getitem_72 / 1.0;  getitem_72 = None
2025-03-14T05:14:27.3554764Z         
2025-03-14T05:14:27.3555150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:27.3555692Z         getitem_73: "f32[4332, 1][4, 4]cpu" = deltas_4[(slice(None, None, None), slice(3, None, 4))];  deltas_4 = None
2025-03-14T05:14:27.3556034Z         dh_8: "f32[4332, 1][1, 1]cpu" = getitem_73 / 1.0;  getitem_73 = None
2025-03-14T05:14:27.3556269Z         
2025-03-14T05:14:27.3556688Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:27.3557216Z         dw_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dw_8, max = 4.135166556742356);  dw_8 = None
2025-03-14T05:14:27.3557476Z         
2025-03-14T05:14:27.3557912Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:27.3558439Z         dh_9: "f32[4332, 1][1, 1]cpu" = torch.clamp(dh_8, max = 4.135166556742356);  dh_8 = None
2025-03-14T05:14:27.3558716Z         
2025-03-14T05:14:27.3559148Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:27.3559688Z         getitem_74: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)]
2025-03-14T05:14:27.3560010Z         mul_42: "f32[4332, 1][1, 1]cpu" = dx_4 * getitem_74;  dx_4 = getitem_74 = None
2025-03-14T05:14:27.3560345Z         getitem_75: "f32[4332, 1][1, 1]cpu" = ctr_x_4[(slice(None, None, None), None)];  ctr_x_4 = None
2025-03-14T05:14:27.3560698Z         pred_ctr_x_4: "f32[4332, 1][1, 1]cpu" = mul_42 + getitem_75;  mul_42 = getitem_75 = None
2025-03-14T05:14:27.3560952Z         
2025-03-14T05:14:27.3561386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:27.3561923Z         getitem_76: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)]
2025-03-14T05:14:27.3562238Z         mul_43: "f32[4332, 1][1, 1]cpu" = dy_4 * getitem_76;  dy_4 = getitem_76 = None
2025-03-14T05:14:27.3562569Z         getitem_77: "f32[4332, 1][1, 1]cpu" = ctr_y_4[(slice(None, None, None), None)];  ctr_y_4 = None
2025-03-14T05:14:27.3562909Z         pred_ctr_y_4: "f32[4332, 1][1, 1]cpu" = mul_43 + getitem_77;  mul_43 = getitem_77 = None
2025-03-14T05:14:27.3563170Z         
2025-03-14T05:14:27.3563592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:27.3564117Z         exp_8: "f32[4332, 1][1, 1]cpu" = torch.exp(dw_9);  dw_9 = None
2025-03-14T05:14:27.3564455Z         getitem_78: "f32[4332, 1][1, 1]cpu" = widths_4[(slice(None, None, None), None)];  widths_4 = None
2025-03-14T05:14:27.3564822Z         pred_w_4: "f32[4332, 1][1, 1]cpu" = exp_8 * getitem_78;  exp_8 = getitem_78 = None
2025-03-14T05:14:27.3565078Z         
2025-03-14T05:14:27.3565515Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:27.3566026Z         exp_9: "f32[4332, 1][1, 1]cpu" = torch.exp(dh_9);  dh_9 = None
2025-03-14T05:14:27.3566370Z         getitem_79: "f32[4332, 1][1, 1]cpu" = heights_4[(slice(None, None, None), None)];  heights_4 = None
2025-03-14T05:14:27.3566732Z         pred_h_4: "f32[4332, 1][1, 1]cpu" = exp_9 * getitem_79;  exp_9 = getitem_79 = None
2025-03-14T05:14:27.3566996Z         
2025-03-14T05:14:27.3567411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:27.3567890Z         mul_46: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4
2025-03-14T05:14:27.3568168Z         x1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 - mul_46;  mul_46 = None
2025-03-14T05:14:27.3568413Z         
2025-03-14T05:14:27.3568812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:27.3569285Z         mul_47: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4
2025-03-14T05:14:27.3569553Z         y1_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 - mul_47;  mul_47 = None
2025-03-14T05:14:27.3569797Z         
2025-03-14T05:14:27.3570200Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:27.3570710Z         mul_48: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_w_4;  pred_w_4 = None
2025-03-14T05:14:27.3571027Z         x2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_x_4 + mul_48;  pred_ctr_x_4 = mul_48 = None
2025-03-14T05:14:27.3571286Z         
2025-03-14T05:14:27.3571765Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:27.3572257Z         mul_49: "f32[4332, 1][1, 1]cpu" = 0.5 * pred_h_4;  pred_h_4 = None
2025-03-14T05:14:27.3572563Z         y2_4: "f32[4332, 1][1, 1]cpu" = pred_ctr_y_4 + mul_49;  pred_ctr_y_4 = mul_49 = None
2025-03-14T05:14:27.3572820Z         
2025-03-14T05:14:27.3573263Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:27.3573862Z         pred_boxes_4: "f32[4332, 1, 4][4, 4, 1]cpu" = torch.stack((x1_4, y1_4, x2_4, y2_4), dim = -1);  x1_4 = y1_4 = x2_4 = y2_4 = None
2025-03-14T05:14:27.3574170Z         
2025-03-14T05:14:27.3574596Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:27.3575155Z         proposals_i_4: "f32[4332, 4][4, 1]cpu" = pred_boxes_4.reshape((4332, 4));  pred_boxes_4 = None
2025-03-14T05:14:27.3575445Z         
2025-03-14T05:14:27.3575924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/rpn.py:532 in _decode_proposals, code: proposals.append(proposals_i.view(N, -1, B))
2025-03-14T05:14:27.3576534Z         proposals_i_9: "f32[4, 1083, 4][4332, 4, 1]cpu" = proposals_i_4.view(4, -1, 4);  proposals_i_4 = None
2025-03-14T05:14:27.3576830Z         
2025-03-14T05:14:27.3577409Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:71 in find_top_rpn_proposals, code: batch_idx = move_device_like(torch.arange(num_images, device=device), proposals[0])
2025-03-14T05:14:27.3578149Z         arange: "i64[4][1]cpu" = torch.arange(4, device = device(type='cpu'))
2025-03-14T05:14:27.3578406Z         
2025-03-14T05:14:27.3578802Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3579301Z         batch_idx: "i64[4][1]cpu" = arange.to(device(type='cpu'));  arange = None
2025-03-14T05:14:27.3579573Z         
2025-03-14T05:14:27.3580114Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:27.3580793Z         topk = l_pred_objectness_logits_0_.topk(1000, dim = 1);  l_pred_objectness_logits_0_ = None
2025-03-14T05:14:27.3581143Z         topk_scores_i: "f32[4, 1000][1000, 1]cpu" = topk[0]
2025-03-14T05:14:27.3581645Z         topk_idx: "i64[4, 1000][1000, 1]cpu" = topk[1];  topk = None
2025-03-14T05:14:27.3581894Z         
2025-03-14T05:14:27.3582441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:27.3583090Z         getitem_82: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:27.3583526Z         topk_proposals_i: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_5[(getitem_82, topk_idx)];  proposals_i_5 = getitem_82 = topk_idx = None
2025-03-14T05:14:27.3583883Z         
2025-03-14T05:14:27.3584513Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:27.3585313Z         full: "i64[1000][1]cpu" = torch.full((1000,), 0, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:27.3585611Z         
2025-03-14T05:14:27.3586009Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3586521Z         to_6: "i64[1000][1]cpu" = full.to(device(type='cpu'));  full = None
2025-03-14T05:14:27.3586770Z         
2025-03-14T05:14:27.3587296Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:27.3587962Z         topk_1 = l_pred_objectness_logits_1_.topk(1000, dim = 1);  l_pred_objectness_logits_1_ = None
2025-03-14T05:14:27.3588307Z         topk_scores_i_1: "f32[4, 1000][1000, 1]cpu" = topk_1[0]
2025-03-14T05:14:27.3588600Z         topk_idx_1: "i64[4, 1000][1000, 1]cpu" = topk_1[1];  topk_1 = None
2025-03-14T05:14:27.3588844Z         
2025-03-14T05:14:27.3589393Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:27.3590041Z         getitem_86: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:27.3590476Z         topk_proposals_i_1: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_6[(getitem_86, topk_idx_1)];  proposals_i_6 = getitem_86 = topk_idx_1 = None
2025-03-14T05:14:27.3590820Z         
2025-03-14T05:14:27.3591350Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:27.3592046Z         full_1: "i64[1000][1]cpu" = torch.full((1000,), 1, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:27.3592336Z         
2025-03-14T05:14:27.3592717Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3593186Z         to_7: "i64[1000][1]cpu" = full_1.to(device(type='cpu'));  full_1 = None
2025-03-14T05:14:27.3593434Z         
2025-03-14T05:14:27.3593980Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:27.3594632Z         topk_2 = l_pred_objectness_logits_2_.topk(1000, dim = 1);  l_pred_objectness_logits_2_ = None
2025-03-14T05:14:27.3594971Z         topk_scores_i_2: "f32[4, 1000][1000, 1]cpu" = topk_2[0]
2025-03-14T05:14:27.3595257Z         topk_idx_2: "i64[4, 1000][1000, 1]cpu" = topk_2[1];  topk_2 = None
2025-03-14T05:14:27.3595499Z         
2025-03-14T05:14:27.3596037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:27.3596675Z         getitem_90: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:27.3597101Z         topk_proposals_i_2: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_7[(getitem_90, topk_idx_2)];  proposals_i_7 = getitem_90 = topk_idx_2 = None
2025-03-14T05:14:27.3597450Z         
2025-03-14T05:14:27.3597985Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:27.3598652Z         full_2: "i64[1000][1]cpu" = torch.full((1000,), 2, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:27.3598959Z         
2025-03-14T05:14:27.3599336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3599822Z         to_8: "i64[1000][1]cpu" = full_2.to(device(type='cpu'));  full_2 = None
2025-03-14T05:14:27.3600064Z         
2025-03-14T05:14:27.3600577Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:27.3601223Z         topk_3 = l_pred_objectness_logits_3_.topk(1000, dim = 1);  l_pred_objectness_logits_3_ = None
2025-03-14T05:14:27.3601558Z         topk_scores_i_3: "f32[4, 1000][1000, 1]cpu" = topk_3[0]
2025-03-14T05:14:27.3601834Z         topk_idx_3: "i64[4, 1000][1000, 1]cpu" = topk_3[1];  topk_3 = None
2025-03-14T05:14:27.3602077Z         
2025-03-14T05:14:27.3602617Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:27.3603250Z         getitem_94: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)]
2025-03-14T05:14:27.3603668Z         topk_proposals_i_3: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_8[(getitem_94, topk_idx_3)];  proposals_i_8 = getitem_94 = topk_idx_3 = None
2025-03-14T05:14:27.3604017Z         
2025-03-14T05:14:27.3604550Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:27.3605214Z         full_3: "i64[1000][1]cpu" = torch.full((1000,), 3, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:27.3605518Z         
2025-03-14T05:14:27.3605903Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3606375Z         to_9: "i64[1000][1]cpu" = full_3.to(device(type='cpu'));  full_3 = None
2025-03-14T05:14:27.3606625Z         
2025-03-14T05:14:27.3607158Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:79 in find_top_rpn_proposals, code: topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
2025-03-14T05:14:27.3607805Z         topk_4 = l_pred_objectness_logits_4_.topk(1000, dim = 1);  l_pred_objectness_logits_4_ = None
2025-03-14T05:14:27.3608136Z         topk_scores_i_4: "f32[4, 1000][1000, 1]cpu" = topk_4[0]
2025-03-14T05:14:27.3608413Z         topk_idx_4: "i64[4, 1000][1000, 1]cpu" = topk_4[1];  topk_4 = None
2025-03-14T05:14:27.3608654Z         
2025-03-14T05:14:27.3609191Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:82 in find_top_rpn_proposals, code: topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 4
2025-03-14T05:14:27.3609852Z         getitem_98: "i64[4, 1][1, 1]cpu" = batch_idx[(slice(None, None, None), None)];  batch_idx = None
2025-03-14T05:14:27.3610298Z         topk_proposals_i_4: "f32[4, 1000, 4][4000, 4, 1]cpu" = proposals_i_9[(getitem_98, topk_idx_4)];  proposals_i_9 = getitem_98 = topk_idx_4 = None
2025-03-14T05:14:27.3610645Z         
2025-03-14T05:14:27.3611179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:88 in find_top_rpn_proposals, code: torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device),
2025-03-14T05:14:27.3611844Z         full_4: "i64[1000][1]cpu" = torch.full((1000,), 4, dtype = torch.int64, device = device(type='cpu'))
2025-03-14T05:14:27.3612130Z         
2025-03-14T05:14:27.3612529Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:177 in move_device_like, code: return src.to(dst.device)
2025-03-14T05:14:27.3613013Z         to_10: "i64[1000][1]cpu" = full_4.to(device(type='cpu'));  full_4 = None
2025-03-14T05:14:27.3613279Z         
2025-03-14T05:14:27.3613646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:27.3614355Z         topk_scores: "f32[4, 5000][5000, 1]cpu" = torch.cat([topk_scores_i, topk_scores_i_1, topk_scores_i_2, topk_scores_i_3, topk_scores_i_4], 1);  topk_scores_i = topk_scores_i_1 = topk_scores_i_2 = topk_scores_i_3 = topk_scores_i_4 = None
2025-03-14T05:14:27.3614840Z         
2025-03-14T05:14:27.3615203Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:27.3615993Z         topk_proposals: "f32[4, 5000, 4][20000, 4, 1]cpu" = torch.cat([topk_proposals_i, topk_proposals_i_1, topk_proposals_i_2, topk_proposals_i_3, topk_proposals_i_4], 1);  topk_proposals_i = topk_proposals_i_1 = topk_proposals_i_2 = topk_proposals_i_3 = topk_proposals_i_4 = None
2025-03-14T05:14:27.3616564Z         
2025-03-14T05:14:27.3616924Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:27.3617444Z         level_ids: "i64[5000][1]cpu" = torch.cat([to_6, to_7, to_8, to_9, to_10], 0);  to_6 = to_7 = to_8 = to_9 = to_10 = level_ids = None
2025-03-14T05:14:27.3617747Z         
2025-03-14T05:14:27.3618241Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:101 in find_top_rpn_proposals, code: boxes = Boxes(topk_proposals[n])
2025-03-14T05:14:27.3618817Z         getitem_100: "f32[5000, 4][4, 1]cpu" = topk_proposals[0];  topk_proposals = None
2025-03-14T05:14:27.3619100Z         
2025-03-14T05:14:27.3619490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:150 in __init__, code: tensor = tensor.to(torch.float32)
2025-03-14T05:14:27.3619996Z         tensor: "f32[5000, 4][4, 1]cpu" = getitem_100.to(torch.float32);  getitem_100 = None
2025-03-14T05:14:27.3620255Z         
2025-03-14T05:14:27.3620735Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:102 in find_top_rpn_proposals, code: scores_per_img = topk_scores[n]
2025-03-14T05:14:27.3621304Z         scores_per_img: "f32[5000][1]cpu" = topk_scores[0];  topk_scores = None
2025-03-14T05:14:27.3621562Z         
2025-03-14T05:14:27.3622132Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:105 in find_top_rpn_proposals, code: valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
2025-03-14T05:14:27.3622812Z         isfinite: "b8[5000, 4][4, 1]cpu" = torch.isfinite(tensor);  tensor = None
2025-03-14T05:14:27.3623127Z         all_1: "b8[5000][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:27.3623461Z         isfinite_1: "b8[5000][1]cpu" = torch.isfinite(scores_per_img);  scores_per_img = None
2025-03-14T05:14:27.3623808Z         valid_mask: "b8[5000][1]cpu" = all_1 & isfinite_1;  all_1 = isfinite_1 = None
2025-03-14T05:14:27.3624066Z         
2025-03-14T05:14:27.3624614Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/proposal_generator/proposal_utils.py:106 in find_top_rpn_proposals, code: if not valid_mask.all():
2025-03-14T05:14:27.3625171Z         all_2: "b8[][]cpu" = valid_mask.all();  valid_mask = all_2 = None
2025-03-14T05:14:27.3625416Z         
2025-03-14T05:14:34.0173119Z 
2025-03-14T05:14:34.0173826Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.0176442Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.0178610Z         l_stack0_ = L_stack0_
2025-03-14T05:14:34.0178973Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:14:34.0179461Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:14:34.0179940Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:14:34.0180470Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:14:34.0180997Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:14:34.0182118Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:14:34.0183028Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:14:34.0183611Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:14:34.0184120Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0184873Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0185451Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0185872Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0186173Z         
2025-03-14T05:14:34.0186580Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:14:34.0187085Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:14:34.0187806Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:14:34.0188552Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:14:34.0189283Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:14:34.0190010Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:14:34.0190360Z         
2025-03-14T05:14:34.0190785Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:14:34.0191769Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:14:34.0192532Z         
2025-03-14T05:14:34.0192971Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:14:34.0194086Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:14:34.0194884Z         
2025-03-14T05:14:34.0195277Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0195760Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0196025Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.0196269Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.0196555Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0196815Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.0197057Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.0197366Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0197623Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.0197865Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.0198142Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0198399Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.0198638Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.0198863Z         
2025-03-14T05:14:34.0199267Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.0200064Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.0200624Z         
2025-03-14T05:14:34.0201099Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.0201691Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:14:34.0201976Z         
2025-03-14T05:14:34.0202387Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.0202927Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.0203220Z         
2025-03-14T05:14:34.0203630Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.0204149Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.0204499Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0204838Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.0205123Z         
2025-03-14T05:14:34.0205526Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.0206029Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.0206343Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.0206680Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.0206956Z         
2025-03-14T05:14:34.0207345Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.0207850Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0208129Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.0208412Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.0208662Z         
2025-03-14T05:14:34.0209061Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.0209576Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.0209883Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.0210173Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.0210448Z         
2025-03-14T05:14:34.0210879Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.0211393Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.0211729Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.0211973Z         
2025-03-14T05:14:34.0212386Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.0212893Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.0213219Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.0213455Z         
2025-03-14T05:14:34.0213848Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.0214358Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.0214679Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.0214917Z         
2025-03-14T05:14:34.0215311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.0215853Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.0216203Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.0216435Z         
2025-03-14T05:14:34.0216867Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.0217419Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.0217683Z         
2025-03-14T05:14:34.0218104Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.0218643Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.0218898Z         
2025-03-14T05:14:34.0219336Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.0219882Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.0220206Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.0220542Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.0220898Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.0221160Z         
2025-03-14T05:14:34.0221595Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.0222142Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.0222462Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.0222796Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.0223140Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.0223456Z         
2025-03-14T05:14:34.0223885Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.0224518Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.0224883Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.0225251Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.0225527Z         
2025-03-14T05:14:34.0225950Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.0226455Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.0226798Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.0227158Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.0227415Z         
2025-03-14T05:14:34.0227816Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.0228295Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.0228556Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.0228800Z         
2025-03-14T05:14:34.0229285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.0229755Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.0230016Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.0230255Z         
2025-03-14T05:14:34.0230675Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.0231152Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.0231468Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.0231720Z         
2025-03-14T05:14:34.0232150Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.0232621Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.0232904Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.0233146Z         
2025-03-14T05:14:34.0233584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.0234167Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.0234461Z         
2025-03-14T05:14:34.0234881Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.0235435Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.0235725Z         
2025-03-14T05:14:34.0236173Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.0236846Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.0237293Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.0237581Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.0237875Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.0238181Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.0238430Z         
2025-03-14T05:14:34.0238822Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0239384Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0239738Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.0239988Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.0240362Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0240735Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.0240975Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.0241342Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0241688Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.0241929Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.0242300Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0242651Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.0242924Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.0243152Z         
2025-03-14T05:14:34.0243601Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.0244158Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:14:34.0244446Z         
2025-03-14T05:14:34.0244920Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.0245593Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.0246013Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.0246301Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.0246595Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.0246906Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.0247164Z         
2025-03-14T05:14:34.0247723Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.0248430Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.0248766Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.0249102Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.0249439Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.0249728Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.0249971Z         
2025-03-14T05:14:34.0250441Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.0250967Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.0251207Z         
2025-03-14T05:14:34.0256586Z 
2025-03-14T05:14:34.0257049Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.0259219Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.0261422Z         l_stack0_ = L_stack0_
2025-03-14T05:14:34.0261795Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:14:34.0262297Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:14:34.0262792Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:14:34.0263322Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:14:34.0263863Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:14:34.0264618Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:14:34.0265218Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:14:34.0265811Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:14:34.0266307Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0266723Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0267133Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0267532Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0267839Z         
2025-03-14T05:14:34.0268249Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:14:34.0268745Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:14:34.0269467Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:14:34.0270207Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:14:34.0270979Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:14:34.0271709Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:14:34.0271995Z         
2025-03-14T05:14:34.0272422Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:14:34.0273397Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:14:34.0274110Z         
2025-03-14T05:14:34.0274533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:14:34.0275547Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:14:34.0276281Z         
2025-03-14T05:14:34.0276660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0277134Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0277390Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.0277646Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.0277929Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0278186Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.0278474Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.0278751Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0279007Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.0279243Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.0279516Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0279766Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.0280003Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.0280221Z         
2025-03-14T05:14:34.0280594Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.0281380Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.0282479Z         
2025-03-14T05:14:34.0282958Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.0283536Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:14:34.0283812Z         
2025-03-14T05:14:34.0284213Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.0284876Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.0285164Z         
2025-03-14T05:14:34.0285583Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.0286118Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.0286439Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0286814Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.0287086Z         
2025-03-14T05:14:34.0287492Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.0287999Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.0288322Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.0288657Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.0288937Z         
2025-03-14T05:14:34.0289340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.0289836Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0290122Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.0290402Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.0290651Z         
2025-03-14T05:14:34.0291054Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.0291603Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.0291917Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.0292223Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.0292474Z         
2025-03-14T05:14:34.0292890Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.0293406Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.0293735Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.0293976Z         
2025-03-14T05:14:34.0294367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.0294870Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.0295195Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.0295430Z         
2025-03-14T05:14:34.0295830Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.0296359Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.0296678Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.0296910Z         
2025-03-14T05:14:34.0297300Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.0297888Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.0298252Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.0298497Z         
2025-03-14T05:14:34.0298956Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.0299521Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.0299810Z         
2025-03-14T05:14:34.0300251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.0300798Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.0301061Z         
2025-03-14T05:14:34.0301516Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.0302086Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.0302429Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.0302780Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.0303148Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.0303416Z         
2025-03-14T05:14:34.0303883Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.0304536Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.0304909Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.0305260Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.0305649Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.0305926Z         
2025-03-14T05:14:34.0306372Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.0306905Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.0307257Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.0307625Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.0307897Z         
2025-03-14T05:14:34.0308342Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.0308873Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.0309230Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.0309595Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.0309865Z         
2025-03-14T05:14:34.0310285Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.0310816Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.0311094Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.0311375Z         
2025-03-14T05:14:34.0311772Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.0312242Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.0312499Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.0312733Z         
2025-03-14T05:14:34.0313144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.0313623Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.0313916Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.0314171Z         
2025-03-14T05:14:34.0314567Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.0315062Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.0315351Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.0315600Z         
2025-03-14T05:14:34.0316037Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.0316617Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.0316911Z         
2025-03-14T05:14:34.0317334Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.0317887Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.0318195Z         
2025-03-14T05:14:34.0318646Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.0319334Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.0319759Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.0320048Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.0320343Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.0320647Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.0320894Z         
2025-03-14T05:14:34.0321276Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0321834Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0322187Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.0322436Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.0322801Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0323145Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.0323387Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.0323751Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0324093Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.0324326Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.0324708Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0325051Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.0325285Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.0325508Z         
2025-03-14T05:14:34.0325923Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.0326509Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:14:34.0326800Z         
2025-03-14T05:14:34.0338826Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.0339571Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.0340017Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.0340338Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.0340661Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.0340983Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.0341249Z         
2025-03-14T05:14:34.0341843Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.0342561Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.0342915Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.0343337Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.0343688Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.0344015Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.0344354Z         
2025-03-14T05:14:34.0344824Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.0345374Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.0345624Z         
2025-03-14T05:14:34.0345782Z 
2025-03-14T05:14:34.0345881Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.0347813Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.0349873Z         l_stack0_ = L_stack0_
2025-03-14T05:14:34.0350268Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:14:34.0350774Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:14:34.0351277Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:14:34.0351776Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:14:34.0352344Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:14:34.0352939Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:14:34.0353540Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:14:34.0354127Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:14:34.0354630Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0355037Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0355432Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0355826Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0356119Z         
2025-03-14T05:14:34.0356499Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:14:34.0356975Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:14:34.0357695Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:14:34.0358424Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:14:34.0359142Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:14:34.0359851Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:14:34.0360139Z         
2025-03-14T05:14:34.0360547Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:14:34.0361500Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:14:34.0362201Z         
2025-03-14T05:14:34.0362623Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:14:34.0363626Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:14:34.0364379Z         
2025-03-14T05:14:34.0364763Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0365231Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0365493Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.0365735Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.0366033Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0366291Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.0366536Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.0366816Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0367071Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.0367312Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.0367585Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0367826Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.0368063Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.0368292Z         
2025-03-14T05:14:34.0368673Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.0369444Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.0369997Z         
2025-03-14T05:14:34.0370464Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.0371060Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:14:34.0371342Z         
2025-03-14T05:14:34.0371742Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.0372293Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.0372578Z         
2025-03-14T05:14:34.0372974Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.0373483Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.0373803Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0374140Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.0374410Z         
2025-03-14T05:14:34.0374812Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.0375315Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.0375624Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.0375963Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.0376237Z         
2025-03-14T05:14:34.0376636Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.0377151Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0377436Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.0377716Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.0377971Z         
2025-03-14T05:14:34.0378367Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.0378901Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.0379211Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.0379501Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.0379755Z         
2025-03-14T05:14:34.0380166Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.0380681Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.0381022Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.0381279Z         
2025-03-14T05:14:34.0382182Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.0382839Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.0383177Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.0383426Z         
2025-03-14T05:14:34.0383839Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.0384534Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.0384899Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.0385169Z         
2025-03-14T05:14:34.0385586Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.0386193Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.0386559Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.0386804Z         
2025-03-14T05:14:34.0387256Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.0387827Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.0388104Z         
2025-03-14T05:14:34.0388557Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.0389113Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.0389381Z         
2025-03-14T05:14:34.0389846Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.0390423Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.0390765Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.0391123Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.0391545Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.0391818Z         
2025-03-14T05:14:34.0392288Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.0392867Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.0393205Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.0393574Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.0393949Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.0394207Z         
2025-03-14T05:14:34.0394632Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.0395138Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.0395472Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.0395822Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.0396077Z         
2025-03-14T05:14:34.0396501Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.0397003Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.0397345Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.0397700Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.0397968Z         
2025-03-14T05:14:34.0398413Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.0398920Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.0399206Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.0399463Z         
2025-03-14T05:14:34.0399888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.0400368Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.0400634Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.0400871Z         
2025-03-14T05:14:34.0401283Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.0401789Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.0402102Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.0402372Z         
2025-03-14T05:14:34.0402788Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.0403292Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.0403585Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.0403845Z         
2025-03-14T05:14:34.0404281Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.0404878Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.0405171Z         
2025-03-14T05:14:34.0405589Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.0406139Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.0406422Z         
2025-03-14T05:14:34.0406895Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.0407576Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.0407997Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.0408291Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.0408590Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.0408899Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.0409156Z         
2025-03-14T05:14:34.0409533Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0410092Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0410437Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.0410678Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.0411033Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0411375Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.0411632Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.0411995Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0412378Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.0412626Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.0413005Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0413344Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.0413578Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.0413796Z         
2025-03-14T05:14:34.0414220Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.0414785Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:14:34.0415078Z         
2025-03-14T05:14:34.0415522Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.0416187Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.0416610Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.0416899Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.0417194Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.0417501Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.0417754Z         
2025-03-14T05:14:34.0418337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.0419033Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.0419372Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.0419705Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.0420065Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.0420359Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.0420601Z         
2025-03-14T05:14:34.0421045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.0421568Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.0421957Z         
2025-03-14T05:14:34.0440167Z 
2025-03-14T05:14:34.0506507Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.0537143Z     def forward(self, L_stack0_: "f32[4000, 256, 7, 7][12544, 49, 7, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_weight_: "f32[1024, 12544][12544, 1]cpu", L_self_modules_box_head_modules_fc1_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_head_modules_fc2_parameters_weight_: "f32[1024, 1024][1024, 1]cpu", L_self_modules_box_head_modules_fc2_parameters_bias_: "f32[1024][1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_weight_: "f32[81, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_cls_score_parameters_bias_: "f32[81][1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_: "f32[320, 1024][1024, 1]cpu", L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_: "f32[320][1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.0539308Z         l_stack0_ = L_stack0_
2025-03-14T05:14:34.0585862Z         l_self_modules_box_head_modules_fc1_parameters_weight_ = L_self_modules_box_head_modules_fc1_parameters_weight_
2025-03-14T05:14:34.0586519Z         l_self_modules_box_head_modules_fc1_parameters_bias_ = L_self_modules_box_head_modules_fc1_parameters_bias_
2025-03-14T05:14:34.0587268Z         l_self_modules_box_head_modules_fc2_parameters_weight_ = L_self_modules_box_head_modules_fc2_parameters_weight_
2025-03-14T05:14:34.0587974Z         l_self_modules_box_head_modules_fc2_parameters_bias_ = L_self_modules_box_head_modules_fc2_parameters_bias_
2025-03-14T05:14:34.0588611Z         l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = L_self_modules_box_predictor_modules_cls_score_parameters_weight_
2025-03-14T05:14:34.0589192Z         l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = L_self_modules_box_predictor_modules_cls_score_parameters_bias_
2025-03-14T05:14:34.0589817Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_weight_
2025-03-14T05:14:34.0590379Z         l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = L_self_modules_box_predictor_modules_bbox_pred_parameters_bias_
2025-03-14T05:14:34.0590862Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0591265Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0591660Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0592345Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.0592652Z         
2025-03-14T05:14:34.0593070Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/box_head.py:96 in forward, code: x = layer(x)
2025-03-14T05:14:34.0593586Z         x: "f32[4000, 12544][12544, 1]cpu" = l_stack0_.flatten(1, -1);  l_stack0_ = None
2025-03-14T05:14:34.0594443Z         x_1: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x, l_self_modules_box_head_modules_fc1_parameters_weight_, l_self_modules_box_head_modules_fc1_parameters_bias_);  x = l_self_modules_box_head_modules_fc1_parameters_weight_ = l_self_modules_box_head_modules_fc1_parameters_bias_ = None
2025-03-14T05:14:34.0595193Z         x_2: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_1, inplace = False);  x_1 = None
2025-03-14T05:14:34.0595919Z         x_3: "f32[4000, 1024][1024, 1]cpu" = torch._C._nn.linear(x_2, l_self_modules_box_head_modules_fc2_parameters_weight_, l_self_modules_box_head_modules_fc2_parameters_bias_);  x_2 = l_self_modules_box_head_modules_fc2_parameters_weight_ = l_self_modules_box_head_modules_fc2_parameters_bias_ = None
2025-03-14T05:14:34.0596635Z         x_4: "f32[4000, 1024][1024, 1]cpu" = torch.nn.functional.relu(x_3, inplace = False);  x_3 = None
2025-03-14T05:14:34.0596924Z         
2025-03-14T05:14:34.0597340Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:303 in forward, code: scores = self.cls_score(x)
2025-03-14T05:14:34.0598309Z         scores: "f32[4000, 81][81, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_cls_score_parameters_weight_, l_self_modules_box_predictor_modules_cls_score_parameters_bias_);  l_self_modules_box_predictor_modules_cls_score_parameters_weight_ = l_self_modules_box_predictor_modules_cls_score_parameters_bias_ = None
2025-03-14T05:14:34.0599020Z         
2025-03-14T05:14:34.0599476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:304 in forward, code: proposal_deltas = self.bbox_pred(x)
2025-03-14T05:14:34.0600476Z         proposal_deltas: "f32[4000, 320][320, 1]cpu" = torch._C._nn.linear(x_4, l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_, l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_);  x_4 = l_self_modules_box_predictor_modules_bbox_pred_parameters_weight_ = l_self_modules_box_predictor_modules_bbox_pred_parameters_bias_ = None
2025-03-14T05:14:34.0601255Z         
2025-03-14T05:14:34.0601640Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0602109Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0602367Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.0602598Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.0602879Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0603127Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.0603362Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.0603631Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0603870Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.0604101Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.0604360Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.0604602Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.0604833Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.0605051Z         
2025-03-14T05:14:34.0605420Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.0606209Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.0606749Z         
2025-03-14T05:14:34.0607209Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.0607808Z         deltas: "f32[4000, 320][320, 1]cpu" = proposal_deltas.float();  proposal_deltas = None
2025-03-14T05:14:34.0608085Z         
2025-03-14T05:14:34.0608490Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.0609033Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.0609312Z         
2025-03-14T05:14:34.0609716Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.0610225Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.0610542Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0610880Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.0611142Z         
2025-03-14T05:14:34.0611543Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.0612039Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.0612341Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.0612691Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.0612965Z         
2025-03-14T05:14:34.0613353Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.0613860Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.0614139Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.0614414Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.0614659Z         
2025-03-14T05:14:34.0615058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.0615567Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.0615875Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.0616156Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.0616404Z         
2025-03-14T05:14:34.0616814Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.0617320Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.0617640Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.0617873Z         
2025-03-14T05:14:34.0618258Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.0618760Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.0619101Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.0619336Z         
2025-03-14T05:14:34.0619721Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.0620228Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.0620562Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.0620795Z         
2025-03-14T05:14:34.0621188Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.0621720Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.0622066Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.0622298Z         
2025-03-14T05:14:34.0622722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.0623256Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.0623504Z         
2025-03-14T05:14:34.0623926Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.0624598Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.0624860Z         
2025-03-14T05:14:34.0625309Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.0625880Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.0626208Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.0626566Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.0626910Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.0627170Z         
2025-03-14T05:14:34.0627604Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.0628141Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.0628458Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.0628787Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.0629126Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.0629387Z         
2025-03-14T05:14:34.0629804Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.0630308Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.0630641Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.0630984Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.0631235Z         
2025-03-14T05:14:34.0631658Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.0632179Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.0632516Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.0632868Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.0633122Z         
2025-03-14T05:14:34.0633538Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.0634006Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.0634271Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.0634542Z         
2025-03-14T05:14:34.0634943Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.0635404Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.0635664Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.0635901Z         
2025-03-14T05:14:34.0636295Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.0636769Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.0637064Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.0637315Z         
2025-03-14T05:14:34.0637707Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.0638183Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.0638491Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.0638739Z         
2025-03-14T05:14:34.0639177Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.0639755Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.0640041Z         
2025-03-14T05:14:34.0640442Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.0640986Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.0641260Z         
2025-03-14T05:14:34.0641699Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.0642352Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.0642762Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.0643042Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.0643332Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.0643628Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.0643874Z         
2025-03-14T05:14:34.0644251Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.0644829Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0645174Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.0645415Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.0645775Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0646111Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.0646350Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.0646727Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0647062Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.0647485Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.0647855Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.0648193Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.0648424Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.0648642Z         
2025-03-14T05:14:34.0649056Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.0649602Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(scores, dim = -1);  scores = None
2025-03-14T05:14:34.0649891Z         
2025-03-14T05:14:34.0650326Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.0650967Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.0651372Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.0651670Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.0651961Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.0652272Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.0652517Z         
2025-03-14T05:14:34.0653058Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.0653728Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.0654058Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.0654381Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.0654713Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.0654995Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.0655228Z         
2025-03-14T05:14:34.0655660Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.0656174Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.0656399Z         
2025-03-14T05:14:34.5369676Z 
2025-03-14T05:14:34.5370356Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.5371441Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.5373420Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:14:34.5373672Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:14:34.5374109Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5380165Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5382874Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5383404Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5383708Z         
2025-03-14T05:14:34.5384144Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5384734Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5385012Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.5385260Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.5385553Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5385826Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.5386080Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.5386368Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5386636Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.5386875Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.5387144Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5387394Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.5387628Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.5387854Z         
2025-03-14T05:14:34.5388311Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.5389126Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.5389765Z         
2025-03-14T05:14:34.5390240Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.5390818Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:14:34.5391097Z         
2025-03-14T05:14:34.5391500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.5392035Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.5392325Z         
2025-03-14T05:14:34.5392736Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.5393246Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.5393570Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5393907Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.5394182Z         
2025-03-14T05:14:34.5394592Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.5395133Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.5395453Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.5395802Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.5396083Z         
2025-03-14T05:14:34.5396503Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.5397001Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5397288Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.5397569Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.5397825Z         
2025-03-14T05:14:34.5398230Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.5398752Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.5399066Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.5399361Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.5399613Z         
2025-03-14T05:14:34.5400031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.5400547Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.5400880Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.5401120Z         
2025-03-14T05:14:34.5401534Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.5402050Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.5402398Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.5402633Z         
2025-03-14T05:14:34.5403020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.5403526Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.5403845Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.5404076Z         
2025-03-14T05:14:34.5404469Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.5405008Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.5405351Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.5405598Z         
2025-03-14T05:14:34.5406020Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.5406560Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.5406821Z         
2025-03-14T05:14:34.5407239Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.5407769Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.5408045Z         
2025-03-14T05:14:34.5408477Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.5409015Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.5409342Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.5409701Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.5410063Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.5410335Z         
2025-03-14T05:14:34.5410782Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.5411344Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.5411670Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.5412011Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.5412361Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.5412623Z         
2025-03-14T05:14:34.5413045Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.5413550Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.5413881Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.5414245Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.5414504Z         
2025-03-14T05:14:34.5414931Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.5415492Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.5415841Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.5416202Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.5416463Z         
2025-03-14T05:14:34.5416864Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.5417327Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.5417600Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.5417837Z         
2025-03-14T05:14:34.5418226Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.5418692Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.5418953Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.5419190Z         
2025-03-14T05:14:34.5419584Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.5420068Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.5420365Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.5420634Z         
2025-03-14T05:14:34.5421031Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.5421506Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.5421797Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.5422046Z         
2025-03-14T05:14:34.5422525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.5423110Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.5423408Z         
2025-03-14T05:14:34.5423831Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.5424492Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.5424805Z         
2025-03-14T05:14:34.5425271Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.5425977Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.5426409Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.5426701Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.5427003Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.5427314Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.5427574Z         
2025-03-14T05:14:34.5427975Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5428544Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5428934Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.5429182Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.5429558Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5429908Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.5430159Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.5430528Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5430893Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.5431134Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.5431545Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5431895Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.5432129Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.5432347Z         
2025-03-14T05:14:34.5432786Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.5433390Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:14:34.5433726Z         
2025-03-14T05:14:34.5434179Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.5434867Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.5435280Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.5435575Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.5435875Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.5436195Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.5436451Z         
2025-03-14T05:14:34.5437003Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.5437696Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.5438042Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.5438389Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.5438727Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.5439017Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.5439259Z         
2025-03-14T05:14:34.5439696Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.5440220Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.5440454Z         
2025-03-14T05:14:34.5466400Z 
2025-03-14T05:14:34.5468905Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.5470002Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.5471129Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:14:34.5471374Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:14:34.5471712Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5472164Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5472656Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5473130Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5473508Z         
2025-03-14T05:14:34.5473995Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5474545Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5474834Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.5475090Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.5475427Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5475710Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.5475970Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.5476281Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5476580Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.5476819Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.5477187Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5477481Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.5477723Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.5477939Z         
2025-03-14T05:14:34.5478316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.5479111Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.5479640Z         
2025-03-14T05:14:34.5480093Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.5480654Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:14:34.5480923Z         
2025-03-14T05:14:34.5481316Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.5482097Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.5482388Z         
2025-03-14T05:14:34.5482801Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.5483316Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.5483640Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5483971Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.5484318Z         
2025-03-14T05:14:34.5484728Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.5485264Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.5485579Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.5485921Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.5486196Z         
2025-03-14T05:14:34.5486588Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.5487083Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5487370Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.5487646Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.5487892Z         
2025-03-14T05:14:34.5488290Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.5488803Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.5489224Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.5489581Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.5489835Z         
2025-03-14T05:14:34.5490254Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.5490800Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.5491135Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.5491377Z         
2025-03-14T05:14:34.5491771Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.5492276Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.5492632Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.5492864Z         
2025-03-14T05:14:34.5493265Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.5493786Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.5494172Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.5494487Z         
2025-03-14T05:14:34.5494886Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.5495440Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.5495794Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.5496036Z         
2025-03-14T05:14:34.5496472Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.5497016Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.5497282Z         
2025-03-14T05:14:34.5497733Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.5498269Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.5498550Z         
2025-03-14T05:14:34.5498993Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.5499549Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.5499971Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.5500454Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.5500811Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.5501078Z         
2025-03-14T05:14:34.5501525Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.5502079Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.5502406Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.5502743Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.5503090Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.5503350Z         
2025-03-14T05:14:34.5503776Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.5504461Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.5504818Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.5505194Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.5505453Z         
2025-03-14T05:14:34.5505888Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.5506422Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.5506778Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.5507147Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.5507409Z         
2025-03-14T05:14:34.5507827Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.5508308Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.5508584Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.5508833Z         
2025-03-14T05:14:34.5509232Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.5509688Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.5509970Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.5510204Z         
2025-03-14T05:14:34.5510598Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.5511088Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.5511381Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.5511631Z         
2025-03-14T05:14:34.5512029Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.5512510Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.5512801Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.5513050Z         
2025-03-14T05:14:34.5513487Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.5514068Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.5514364Z         
2025-03-14T05:14:34.5514787Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.5515343Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.5515633Z         
2025-03-14T05:14:34.5516078Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.5516752Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.5517171Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.5517457Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.5517775Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.5518078Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.5518331Z         
2025-03-14T05:14:34.5518712Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5519265Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5519628Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.5519878Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.5520244Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5520585Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.5520826Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.5521186Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5521531Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.5521770Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.5522166Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5522499Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.5522730Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.5522945Z         
2025-03-14T05:14:34.5523358Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.5523943Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:14:34.5524263Z         
2025-03-14T05:14:34.5524722Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.5525381Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.5525785Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.5526061Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.5526342Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.5526634Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.5526875Z         
2025-03-14T05:14:34.5527411Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.5528083Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.5528410Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.5528730Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.5529050Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.5529330Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.5529562Z         
2025-03-14T05:14:34.5529986Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.5530508Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.5530742Z         
2025-03-14T05:14:34.5550810Z 
2025-03-14T05:14:34.5556009Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.5557075Z     def forward(self, L_predictions_0_: "f32[4000, 81][81, 1]cpu", L_predictions_1_: "f32[4000, 320][320, 1]cpu", s0: "Sym(s0)", L_proposals_0_fields_proposal_boxes_tensor: "f32[s0, 4][4, 1]cpu", s1: "Sym(s1)", L_proposals_1_fields_proposal_boxes_tensor: "f32[s1, 4][4, 1]cpu", s2: "Sym(s2)", L_proposals_2_fields_proposal_boxes_tensor: "f32[s2, 4][4, 1]cpu", s3: "Sym(s3)", L_proposals_3_fields_proposal_boxes_tensor: "f32[s3, 4][4, 1]cpu"):
2025-03-14T05:14:34.5557921Z         l_predictions_0_ = L_predictions_0_
2025-03-14T05:14:34.5558160Z         l_predictions_1_ = L_predictions_1_
2025-03-14T05:14:34.5558481Z         l_proposals_0_fields_proposal_boxes_tensor = L_proposals_0_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5558892Z         l_proposals_1_fields_proposal_boxes_tensor = L_proposals_1_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5559304Z         l_proposals_2_fields_proposal_boxes_tensor = L_proposals_2_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5559709Z         l_proposals_3_fields_proposal_boxes_tensor = L_proposals_3_fields_proposal_boxes_tensor
2025-03-14T05:14:34.5560012Z         
2025-03-14T05:14:34.5560448Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5560939Z         size = l_proposals_0_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5561203Z         getitem: "Sym(s0)" = size[0]
2025-03-14T05:14:34.5561448Z         getitem_1 = size[1];  size = getitem_1 = None
2025-03-14T05:14:34.5561742Z         size_1 = l_proposals_1_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5562098Z         getitem_2: "Sym(s1)" = size_1[0]
2025-03-14T05:14:34.5562381Z         getitem_3 = size_1[1];  size_1 = getitem_3 = None
2025-03-14T05:14:34.5562712Z         size_2 = l_proposals_2_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5562975Z         getitem_4: "Sym(s2)" = size_2[0]
2025-03-14T05:14:34.5563215Z         getitem_5 = size_2[1];  size_2 = getitem_5 = None
2025-03-14T05:14:34.5563495Z         size_3 = l_proposals_3_fields_proposal_boxes_tensor.size()
2025-03-14T05:14:34.5563784Z         getitem_6: "Sym(s3)" = size_3[0]
2025-03-14T05:14:34.5564023Z         getitem_7 = size_3[1];  size_3 = getitem_7 = None
2025-03-14T05:14:34.5564250Z         
2025-03-14T05:14:34.5564645Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/layers/wrappers.py:72 in cat, code: return torch.cat(tensors, dim)
2025-03-14T05:14:34.5565455Z         proposal_boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = torch.cat([l_proposals_0_fields_proposal_boxes_tensor, l_proposals_1_fields_proposal_boxes_tensor, l_proposals_2_fields_proposal_boxes_tensor, l_proposals_3_fields_proposal_boxes_tensor], 0)
2025-03-14T05:14:34.5566020Z         
2025-03-14T05:14:34.5566500Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:88 in apply_deltas, code: deltas = deltas.float()  # ensure fp32 for decoding precision
2025-03-14T05:14:34.5567086Z         deltas: "f32[4000, 320][320, 1]cpu" = l_predictions_1_.float();  l_predictions_1_ = None
2025-03-14T05:14:34.5567372Z         
2025-03-14T05:14:34.5567780Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:89 in apply_deltas, code: boxes = boxes.to(deltas.dtype)
2025-03-14T05:14:34.5568356Z         boxes: "f32[s0 + s1 + s2 + s3, 4][4, 1]cpu" = proposal_boxes.to(torch.float32);  proposal_boxes = None
2025-03-14T05:14:34.5568651Z         
2025-03-14T05:14:34.5569059Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:91 in apply_deltas, code: widths = boxes[:, 2] - boxes[:, 0]
2025-03-14T05:14:34.5569600Z         getitem_8: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 2)]
2025-03-14T05:14:34.5569923Z         getitem_9: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5570255Z         widths: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_8 - getitem_9;  getitem_8 = getitem_9 = None
2025-03-14T05:14:34.5570526Z         
2025-03-14T05:14:34.5570935Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:92 in apply_deltas, code: heights = boxes[:, 3] - boxes[:, 1]
2025-03-14T05:14:34.5571457Z         getitem_10: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 3)]
2025-03-14T05:14:34.5571773Z         getitem_11: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)]
2025-03-14T05:14:34.5572109Z         heights: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_10 - getitem_11;  getitem_10 = getitem_11 = None
2025-03-14T05:14:34.5572379Z         
2025-03-14T05:14:34.5572778Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:93 in apply_deltas, code: ctr_x = boxes[:, 0] + 0.5 * widths
2025-03-14T05:14:34.5573268Z         getitem_12: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 0)]
2025-03-14T05:14:34.5573551Z         mul: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * widths
2025-03-14T05:14:34.5573827Z         ctr_x: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_12 + mul;  getitem_12 = mul = None
2025-03-14T05:14:34.5574076Z         
2025-03-14T05:14:34.5574476Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:94 in apply_deltas, code: ctr_y = boxes[:, 1] + 0.5 * heights
2025-03-14T05:14:34.5574987Z         getitem_13: "f32[s0 + s1 + s2 + s3][4]cpu" = boxes[(slice(None, None, None), 1)];  boxes = None
2025-03-14T05:14:34.5575295Z         mul_1: "f32[s0 + s1 + s2 + s3][1]cpu" = 0.5 * heights
2025-03-14T05:14:34.5575578Z         ctr_y: "f32[s0 + s1 + s2 + s3][1]cpu" = getitem_13 + mul_1;  getitem_13 = mul_1 = None
2025-03-14T05:14:34.5575830Z         
2025-03-14T05:14:34.5576257Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:97 in apply_deltas, code: dx = deltas[:, 0::4] / wx
2025-03-14T05:14:34.5576773Z         getitem_14: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(0, None, 4))]
2025-03-14T05:14:34.5577120Z         dx: "f32[4000, 80][80, 1]cpu" = getitem_14 / 10.0;  getitem_14 = None
2025-03-14T05:14:34.5577358Z         
2025-03-14T05:14:34.5577748Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:98 in apply_deltas, code: dy = deltas[:, 1::4] / wy
2025-03-14T05:14:34.5578258Z         getitem_15: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(1, None, 4))]
2025-03-14T05:14:34.5578581Z         dy: "f32[4000, 80][80, 1]cpu" = getitem_15 / 10.0;  getitem_15 = None
2025-03-14T05:14:34.5578820Z         
2025-03-14T05:14:34.5579216Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:99 in apply_deltas, code: dw = deltas[:, 2::4] / ww
2025-03-14T05:14:34.5579722Z         getitem_16: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(2, None, 4))]
2025-03-14T05:14:34.5580051Z         dw: "f32[4000, 80][80, 1]cpu" = getitem_16 / 5.0;  getitem_16 = None
2025-03-14T05:14:34.5580280Z         
2025-03-14T05:14:34.5580670Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:100 in apply_deltas, code: dh = deltas[:, 3::4] / wh
2025-03-14T05:14:34.5581206Z         getitem_17: "f32[4000, 80][320, 4]cpu" = deltas[(slice(None, None, None), slice(3, None, 4))];  deltas = None
2025-03-14T05:14:34.5581766Z         dh: "f32[4000, 80][80, 1]cpu" = getitem_17 / 5.0;  getitem_17 = None
2025-03-14T05:14:34.5582004Z         
2025-03-14T05:14:34.5582429Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:103 in apply_deltas, code: dw = torch.clamp(dw, max=self.scale_clamp)
2025-03-14T05:14:34.5583054Z         dw_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dw, max = 4.135166556742356);  dw = None
2025-03-14T05:14:34.5583305Z         
2025-03-14T05:14:34.5583725Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:104 in apply_deltas, code: dh = torch.clamp(dh, max=self.scale_clamp)
2025-03-14T05:14:34.5584351Z         dh_1: "f32[4000, 80][80, 1]cpu" = torch.clamp(dh, max = 4.135166556742356);  dh = None
2025-03-14T05:14:34.5584621Z         
2025-03-14T05:14:34.5585109Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:106 in apply_deltas, code: pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
2025-03-14T05:14:34.5585668Z         getitem_18: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)]
2025-03-14T05:14:34.5585989Z         mul_2: "f32[4000, 80][80, 1]cpu" = dx * getitem_18;  dx = getitem_18 = None
2025-03-14T05:14:34.5586329Z         getitem_19: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_x[(slice(None, None, None), None)];  ctr_x = None
2025-03-14T05:14:34.5586686Z         pred_ctr_x: "f32[4000, 80][80, 1]cpu" = mul_2 + getitem_19;  mul_2 = getitem_19 = None
2025-03-14T05:14:34.5586941Z         
2025-03-14T05:14:34.5587374Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:107 in apply_deltas, code: pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
2025-03-14T05:14:34.5587909Z         getitem_20: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)]
2025-03-14T05:14:34.5588229Z         mul_3: "f32[4000, 80][80, 1]cpu" = dy * getitem_20;  dy = getitem_20 = None
2025-03-14T05:14:34.5588557Z         getitem_21: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = ctr_y[(slice(None, None, None), None)];  ctr_y = None
2025-03-14T05:14:34.5588898Z         pred_ctr_y: "f32[4000, 80][80, 1]cpu" = mul_3 + getitem_21;  mul_3 = getitem_21 = None
2025-03-14T05:14:34.5589155Z         
2025-03-14T05:14:34.5589603Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:108 in apply_deltas, code: pred_w = torch.exp(dw) * widths[:, None]
2025-03-14T05:14:34.5590135Z         exp: "f32[4000, 80][80, 1]cpu" = torch.exp(dw_1);  dw_1 = None
2025-03-14T05:14:34.5590473Z         getitem_22: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = widths[(slice(None, None, None), None)];  widths = None
2025-03-14T05:14:34.5590822Z         pred_w: "f32[4000, 80][80, 1]cpu" = exp * getitem_22;  exp = getitem_22 = None
2025-03-14T05:14:34.5591074Z         
2025-03-14T05:14:34.5591494Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:109 in apply_deltas, code: pred_h = torch.exp(dh) * heights[:, None]
2025-03-14T05:14:34.5591996Z         exp_1: "f32[4000, 80][80, 1]cpu" = torch.exp(dh_1);  dh_1 = None
2025-03-14T05:14:34.5592336Z         getitem_23: "f32[s0 + s1 + s2 + s3, 1][1, 1]cpu" = heights[(slice(None, None, None), None)];  heights = None
2025-03-14T05:14:34.5592682Z         pred_h: "f32[4000, 80][80, 1]cpu" = exp_1 * getitem_23;  exp_1 = getitem_23 = None
2025-03-14T05:14:34.5592935Z         
2025-03-14T05:14:34.5593337Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:111 in apply_deltas, code: x1 = pred_ctr_x - 0.5 * pred_w
2025-03-14T05:14:34.5593801Z         mul_6: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w
2025-03-14T05:14:34.5594068Z         x1: "f32[4000, 80][80, 1]cpu" = pred_ctr_x - mul_6;  mul_6 = None
2025-03-14T05:14:34.5594300Z         
2025-03-14T05:14:34.5594708Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:112 in apply_deltas, code: y1 = pred_ctr_y - 0.5 * pred_h
2025-03-14T05:14:34.5595312Z         mul_7: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h
2025-03-14T05:14:34.5595578Z         y1: "f32[4000, 80][80, 1]cpu" = pred_ctr_y - mul_7;  mul_7 = None
2025-03-14T05:14:34.5595835Z         
2025-03-14T05:14:34.5596237Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:113 in apply_deltas, code: x2 = pred_ctr_x + 0.5 * pred_w
2025-03-14T05:14:34.5596718Z         mul_8: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_w;  pred_w = None
2025-03-14T05:14:34.5597021Z         x2: "f32[4000, 80][80, 1]cpu" = pred_ctr_x + mul_8;  pred_ctr_x = mul_8 = None
2025-03-14T05:14:34.5597264Z         
2025-03-14T05:14:34.5597694Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:114 in apply_deltas, code: y2 = pred_ctr_y + 0.5 * pred_h
2025-03-14T05:14:34.5598169Z         mul_9: "f32[4000, 80][80, 1]cpu" = 0.5 * pred_h;  pred_h = None
2025-03-14T05:14:34.5598457Z         y2: "f32[4000, 80][80, 1]cpu" = pred_ctr_y + mul_9;  pred_ctr_y = mul_9 = None
2025-03-14T05:14:34.5598703Z         
2025-03-14T05:14:34.5599162Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:115 in apply_deltas, code: pred_boxes = torch.stack((x1, y1, x2, y2), dim=-1)
2025-03-14T05:14:34.5599740Z         pred_boxes: "f32[4000, 80, 4][320, 4, 1]cpu" = torch.stack((x1, y1, x2, y2), dim = -1);  x1 = y1 = x2 = y2 = None
2025-03-14T05:14:34.5600031Z         
2025-03-14T05:14:34.5600453Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/box_regression.py:116 in apply_deltas, code: return pred_boxes.reshape(deltas.shape)
2025-03-14T05:14:34.5601008Z         predict_boxes: "f32[4000, 320][320, 1]cpu" = pred_boxes.reshape((4000, 320));  pred_boxes = None
2025-03-14T05:14:34.5601296Z         
2025-03-14T05:14:34.5601737Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:547 in predict_boxes, code: return predict_boxes.split(num_prop_per_image)
2025-03-14T05:14:34.5603457Z         split = predict_boxes.split([getitem, getitem_2, getitem_4, getitem_6]);  predict_boxes = getitem = getitem_2 = getitem_4 = getitem_6 = None
2025-03-14T05:14:34.5603901Z         boxes_per_image: "f32[s0, 320][320, 1]cpu" = split[0]
2025-03-14T05:14:34.5604187Z         getitem_25: "f32[s1, 320][320, 1]cpu" = split[1];  getitem_25 = None
2025-03-14T05:14:34.5604508Z         getitem_26: "f32[s2, 320][320, 1]cpu" = split[2];  getitem_26 = None
2025-03-14T05:14:34.5604816Z         getitem_27: "f32[s3, 320][320, 1]cpu" = split[3];  split = getitem_27 = None
2025-03-14T05:14:34.5605074Z         
2025-03-14T05:14:34.5605460Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/structures/boxes.py:240 in __len__, code: return self.tensor.shape[0]
2025-03-14T05:14:34.5606026Z         size_4 = l_proposals_0_fields_proposal_boxes_tensor.size();  l_proposals_0_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5606379Z         getitem_28: "Sym(s0)" = size_4[0]
2025-03-14T05:14:34.5606625Z         getitem_29 = size_4[1];  size_4 = getitem_29 = None
2025-03-14T05:14:34.5606993Z         size_5 = l_proposals_1_fields_proposal_boxes_tensor.size();  l_proposals_1_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5607341Z         getitem_30: "Sym(s1)" = size_5[0]
2025-03-14T05:14:34.5607580Z         getitem_31 = size_5[1];  size_5 = getitem_31 = None
2025-03-14T05:14:34.5607945Z         size_6 = l_proposals_2_fields_proposal_boxes_tensor.size();  l_proposals_2_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5608281Z         getitem_32: "Sym(s2)" = size_6[0]
2025-03-14T05:14:34.5608512Z         getitem_33 = size_6[1];  size_6 = getitem_33 = None
2025-03-14T05:14:34.5608866Z         size_7 = l_proposals_3_fields_proposal_boxes_tensor.size();  l_proposals_3_fields_proposal_boxes_tensor = None
2025-03-14T05:14:34.5609203Z         getitem_34: "Sym(s3)" = size_7[0]
2025-03-14T05:14:34.5609437Z         getitem_35 = size_7[1];  size_7 = getitem_35 = None
2025-03-14T05:14:34.5609654Z         
2025-03-14T05:14:34.5610097Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:568 in predict_probs, code: probs = F.softmax(scores, dim=-1)
2025-03-14T05:14:34.5610698Z         probs: "f32[4000, 81][81, 1]cpu" = torch.nn.functional.softmax(l_predictions_0_, dim = -1);  l_predictions_0_ = None
2025-03-14T05:14:34.5611031Z         
2025-03-14T05:14:34.5611475Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:569 in predict_probs, code: return probs.split(num_inst_per_image, dim=0)
2025-03-14T05:14:34.5612159Z         split_1 = probs.split([getitem_28, getitem_30, getitem_32, getitem_34], dim = 0);  probs = getitem_28 = getitem_30 = getitem_32 = getitem_34 = None
2025-03-14T05:14:34.5612578Z         scores_per_image: "f32[s0, 81][81, 1]cpu" = split_1[0]
2025-03-14T05:14:34.5612870Z         getitem_37: "f32[s1, 81][81, 1]cpu" = split_1[1];  getitem_37 = None
2025-03-14T05:14:34.5613165Z         getitem_38: "f32[s2, 81][81, 1]cpu" = split_1[2];  getitem_38 = None
2025-03-14T05:14:34.5613474Z         getitem_39: "f32[s3, 81][81, 1]cpu" = split_1[3];  split_1 = getitem_39 = None
2025-03-14T05:14:34.5613733Z         
2025-03-14T05:14:34.5614287Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.5614975Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(boxes_per_image);  boxes_per_image = None
2025-03-14T05:14:34.5615309Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.5615639Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(scores_per_image);  scores_per_image = None
2025-03-14T05:14:34.5615974Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.5616265Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.5616505Z         
2025-03-14T05:14:34.5616960Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.5617478Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.5617734Z         
2025-03-14T05:14:34.9515986Z 
2025-03-14T05:14:34.9521586Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.9526078Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:14:34.9530589Z         l_scores_0_ = L_scores_0_
2025-03-14T05:14:34.9534876Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:14:34.9537001Z         
2025-03-14T05:14:34.9542628Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.9543656Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:14:34.9544011Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.9544476Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:14:34.9544816Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.9545128Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.9545402Z         
2025-03-14T05:14:34.9545874Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.9546410Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.9546656Z         
2025-03-14T05:14:34.9547134Z 
2025-03-14T05:14:34.9547233Z class GraphModule(torch.nn.Module):
2025-03-14T05:14:34.9547587Z     def forward(self, s0: "Sym(s0)", L_scores_0_: "f32[s0, 81][81, 1]cpu", s1: "Sym(s0)", L_boxes_0_: "f32[s0, 320][320, 1]cpu"):
2025-03-14T05:14:34.9547922Z         l_scores_0_ = L_scores_0_
2025-03-14T05:14:34.9548139Z         l_boxes_0_ = L_boxes_0_
2025-03-14T05:14:34.9548338Z         
2025-03-14T05:14:34.9548994Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:137 in fast_rcnn_inference_single_image, code: valid_mask = torch.isfinite(boxes).all(dim=1) & torch.isfinite(scores).all(dim=1)
2025-03-14T05:14:34.9549689Z         isfinite: "b8[s0, 320][320, 1]cpu" = torch.isfinite(l_boxes_0_);  l_boxes_0_ = None
2025-03-14T05:14:34.9550017Z         all_1: "b8[s0][1]cpu" = isfinite.all(dim = 1);  isfinite = None
2025-03-14T05:14:34.9550353Z         isfinite_1: "b8[s0, 81][81, 1]cpu" = torch.isfinite(l_scores_0_);  l_scores_0_ = None
2025-03-14T05:14:34.9550670Z         all_2: "b8[s0][1]cpu" = isfinite_1.all(dim = 1);  isfinite_1 = None
2025-03-14T05:14:34.9550966Z         valid_mask: "b8[s0][1]cpu" = all_1 & all_2;  all_1 = all_2 = None
2025-03-14T05:14:34.9551209Z         
2025-03-14T05:14:34.9551654Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/detectron2/modeling/roi_heads/fast_rcnn.py:138 in fast_rcnn_inference_single_image, code: if not valid_mask.all():
2025-03-14T05:14:34.9552178Z         all_3: "b8[][]cpu" = valid_mask.all();  valid_mask = all_3 = None
2025-03-14T05:14:34.9552415Z         
2025-03-14T05:14:57.9798363Z Compilation time (from dynamo_timed): 38.632892926
2025-03-14T05:14:57.9799582Z pass
2025-03-14T05:14:57.9800056Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:14:57.9801185Z TIMING: entire_frame_compile:38.63289 gc:0.05461 _recursive_pre_grad_passes:0.04171 async_compile.wait:6.15733 backend_compile:18.64965 _recursive_joint_graph_passes:0.16058 inductor_compile:7.62573 _recursive_post_grad_passes:0.02977 code_gen:7.23456 total_wall_time:38.63289
2025-03-14T05:14:57.9802406Z STATS: call_* op count: 1138 | FakeTensorMode.__torch_dispatch__:16538 | FakeTensor.__torch_dispatch__:437 | ProxyTorchDispatchMode.__torch_dispatch__:1369 | attempt fast:182 | slow no contiguity match:40 | fast is_contiguous:138 | slow both tensors nontrivially broadcast:4
2025-03-14T05:14:57.9807192Z Dynamo produced 78 graphs covering 1138 ops with 62 graph breaks (8 unique)
2025-03-14T05:15:04.1307960Z 
2025-03-14T05:15:10.0863052Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:15:10.0866010Z loading model: 0it [00:05, ?it/s]
2025-03-14T05:15:10.0866435Z cpu  eval  dlrm                               
2025-03-14T05:15:10.7452093Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:10.9820395Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:11.1848402Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:22.0405861Z Compilation time (from dynamo_timed): 9.499951252
2025-03-14T05:15:22.0406225Z pass
2025-03-14T05:15:22.0406525Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:22.0407415Z TIMING: _recursive_pre_grad_passes:0.00416 _recursive_joint_graph_passes:0.10331 _recursive_post_grad_passes:0.01635 async_compile.wait:1.37773 code_gen:8.20009 inductor_compile:8.33759 backend_compile:9.12453 entire_frame_compile:9.49995 gc:0.0014 total_wall_time:9.49995
2025-03-14T05:15:22.0408462Z STATS: call_* op count: 36 | FakeTensorMode.__torch_dispatch__:1716 | ProxyTorchDispatchMode.__torch_dispatch__:504 | FakeTensor.__torch_dispatch__:81
2025-03-14T05:15:22.0409037Z Dynamo produced 1 graphs covering 36 ops with 0 graph breaks (0 unique)
2025-03-14T05:15:26.5206476Z 
2025-03-14T05:15:27.5757344Z loading model: 0it [00:00, ?it/s]Downloading https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-79bd7d70.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/db_resnet50-79bd7d70.pt
2025-03-14T05:15:27.9446166Z 
2025-03-14T05:15:27.9448295Z 
2025-03-14T05:15:28.0446370Z   0% 0/102021912 [00:00<?, ?it/s]
2025-03-14T05:15:28.0446815Z 
2025-03-14T05:15:28.1474222Z  33% 33462272/102021912 [00:00<00:00, 334622241.32it/s]
2025-03-14T05:15:28.1504785Z 
2025-03-14T05:15:28.2325433Z  69% 70711296/102021912 [00:00<00:00, 356896220.15it/s]
2025-03-14T05:15:28.2327906Z 102022144it [00:00, 354633592.76it/s]                  
2025-03-14T05:15:29.4011746Z Downloading https://doctr-static.mindee.com/models?id=v0.3.1/crnn_vgg16_bn-9762b0b0.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/crnn_vgg16_bn-9762b0b0.pt
2025-03-14T05:15:29.7598829Z 
2025-03-14T05:15:29.7599224Z 
2025-03-14T05:15:29.8605581Z   0% 0/63286381 [00:00<?, ?it/s]
2025-03-14T05:15:29.8606217Z 
2025-03-14T05:15:29.9152907Z  66% 41651200/63286381 [00:00<00:00, 416510411.14it/s]
2025-03-14T05:15:29.9154087Z 63287296it [00:00, 421640934.89it/s]                  
2025-03-14T05:15:33.6127853Z 
2025-03-14T05:15:33.6132288Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:15:33.6141220Z cpu  eval  doctr_det_predictor                
2025-03-14T05:15:50.9458970Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:15:57.4251911Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:04.6123335Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:37.4617822Z Compilation time (from dynamo_timed): 29.256619927
2025-03-14T05:16:37.4623320Z pass
2025-03-14T05:16:37.4628540Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:37.4631341Z TIMING: entire_frame_compile:29.25662 gc:0.00876 _recursive_pre_grad_passes:0.00984 _recursive_joint_graph_passes:0.15757 _recursive_post_grad_passes:0.0986 async_compile.wait:3.23973 code_gen:15.74116 inductor_compile:17.46882 backend_compile:21.34525 total_wall_time:29.25662
2025-03-14T05:16:37.4632405Z STATS: call_* op count: 230 | FakeTensorMode.__torch_dispatch__:16961 | ProxyTorchDispatchMode.__torch_dispatch__:6953 | FakeTensor.__torch_dispatch__:2205
2025-03-14T05:16:37.4637058Z Dynamo produced 4 graphs covering 230 ops with 5 graph breaks (2 unique)
2025-03-14T05:16:42.7406176Z 
2025-03-14T05:16:45.6310563Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:16:45.6311177Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:16:45.6338339Z cpu  eval  doctr_reco_predictor               
2025-03-14T05:16:45.8479791Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:46.0256356Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:46.0784420Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:16:58.7416711Z W0314 05:16:58.739732 68704 site-packages/torch/fx/experimental/symbolic_shapes.py:6679] [2/0] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False
2025-03-14T05:16:58.7418158Z E0314 05:16:58.740088 68704 site-packages/torch/fx/experimental/recording.py:299] [2/0] failed while running evaluate_expr(*(u0, None, u0, False), **{})
2025-03-14T05:16:58.8018347Z W0314 05:16:58.800352 68704 site-packages/torch/fx/experimental/symbolic_shapes.py:6679] [3/0] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False
2025-03-14T05:16:58.8024176Z E0314 05:16:58.800680 68704 site-packages/torch/fx/experimental/recording.py:299] [3/0] failed while running evaluate_expr(*(u0, None, u0, False), **{})
2025-03-14T05:17:00.4575898Z Compilation time (from dynamo_timed): 13.259724708
2025-03-14T05:17:00.4578148Z pass
2025-03-14T05:17:00.4578634Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:00.4579730Z TIMING: entire_frame_compile:13.25972 gc:0.00461 _recursive_pre_grad_passes:0.0046 _recursive_joint_graph_passes:0.10138 _recursive_post_grad_passes:0.03416 async_compile.wait:2.78211 code_gen:9.81259 inductor_compile:10.43555 backend_compile:11.61769 total_wall_time:13.25972
2025-03-14T05:17:00.4580657Z STATS: call_* op count: 56 | FakeTensorMode.__torch_dispatch__:3962 | ProxyTorchDispatchMode.__torch_dispatch__:1316 | FakeTensor.__torch_dispatch__:447
2025-03-14T05:17:00.4586238Z Dynamo produced 2 graphs covering 56 ops with 4 graph breaks (3 unique)
2025-03-14T05:17:05.2026053Z 
2025-03-14T05:17:06.4008043Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:17:06.4008481Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:17:06.4008760Z cpu  eval  drq                                
2025-03-14T05:17:06.4116431Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:06.4164430Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:06.4209979Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:16.5531580Z Compilation time (from dynamo_timed): 9.127466661
2025-03-14T05:17:16.5532045Z pass
2025-03-14T05:17:16.5539879Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:16.5540742Z TIMING: _recursive_pre_grad_passes:0.00423 _recursive_joint_graph_passes:0.1012 _recursive_post_grad_passes:0.01706 async_compile.wait:1.58901 code_gen:7.67942 inductor_compile:7.7819 backend_compile:8.55813 entire_frame_compile:9.12747 gc:0.00254 total_wall_time:9.12747
2025-03-14T05:17:16.5541652Z STATS: call_* op count: 58 | FakeTensor.__torch_dispatch__:72 | FakeTensorMode.__torch_dispatch__:1118 | ProxyTorchDispatchMode.__torch_dispatch__:290
2025-03-14T05:17:16.5542151Z Dynamo produced 2 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-14T05:17:21.1145463Z 
2025-03-14T05:17:24.0517959Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:17:24.0520286Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:17:24.0530365Z cpu  eval  fastNLP_Bert                       
2025-03-14T05:17:26.9954451Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:28.2159015Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:28.8913558Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:29.9497284Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:17:29.9499016Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:17:29.9499835Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] or:
2025-03-14T05:17:29.9500362Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:17:29.9500954Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] to include these operations in the captured graph.
2025-03-14T05:17:29.9501450Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:17:29.9501917Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break: from user code at:
2025-03-14T05:17:29.9502581Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 459, in forward_pass
2025-03-14T05:17:29.9503216Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     return mod(*inputs)
2025-03-14T05:17:29.9504345Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/models/bert.py", line 265, in forward
2025-03-14T05:17:29.9505490Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     sequence_output = self.bert(words)
2025-03-14T05:17:29.9506200Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/embeddings/bert_embedding.py", line 137, in forward
2025-03-14T05:17:29.9507035Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     outputs = self.model(words)
2025-03-14T05:17:29.9507731Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/fastNLP/embeddings/bert_embedding.py", line 445, in forward
2025-03-14T05:17:29.9509023Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     max_word_piece_length = batch_word_pieces_length.sum(dim=-1).max().item()  # 表示word piece的长度(包括padding)
2025-03-14T05:17:29.9509650Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:17:29.9510033Z W0314 05:17:29.947969 70578 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:17:49.5291898Z Compilation time (from dynamo_timed): 17.969875324
2025-03-14T05:17:49.5294041Z pass
2025-03-14T05:17:49.5294460Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:49.5295240Z TIMING: entire_frame_compile:17.96988 gc:0.00482 _recursive_pre_grad_passes:0.0091 _recursive_joint_graph_passes:0.41181 _recursive_post_grad_passes:0.05661 async_compile.wait:3.80349 code_gen:11.25341 inductor_compile:12.46349 backend_compile:15.44315 total_wall_time:17.96988
2025-03-14T05:17:49.5296405Z STATS: call_* op count: 440 | FakeTensorMode.__torch_dispatch__:14710 | ProxyTorchDispatchMode.__torch_dispatch__:6105 | FakeTensor.__torch_dispatch__:1148
2025-03-14T05:17:49.5296922Z Dynamo produced 4 graphs covering 440 ops with 4 graph breaks (1 unique)
2025-03-14T05:17:54.5010845Z 
2025-03-14T05:17:54.8934608Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:17:54.8934988Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:17:54.8935343Z cpu  eval  functorch_dp_cifar10               
2025-03-14T05:17:54.9429511Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:54.9968276Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:17:55.1157688Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:08.8356351Z Compilation time (from dynamo_timed): 12.608248835
2025-03-14T05:18:08.8358900Z pass
2025-03-14T05:18:08.8366484Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:08.8367483Z TIMING: _recursive_pre_grad_passes:0.00378 _recursive_joint_graph_passes:0.35152 _recursive_post_grad_passes:0.03107 async_compile.wait:1.88051 code_gen:9.85272 inductor_compile:10.43675 backend_compile:11.83513 entire_frame_compile:12.60825 gc:0.00101 total_wall_time:12.60825
2025-03-14T05:18:08.8368544Z STATS: call_* op count: 69 | FakeTensorMode.__torch_dispatch__:3168 | ProxyTorchDispatchMode.__torch_dispatch__:1158 | FakeTensor.__torch_dispatch__:545
2025-03-14T05:18:08.8369138Z Dynamo produced 1 graphs covering 69 ops with 0 graph breaks (0 unique)
2025-03-14T05:18:13.5611302Z 
2025-03-14T05:18:13.7538434Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:18:13.7538848Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:18:13.7539197Z cpu  eval  functorch_maml_omniglot            
2025-03-14T05:18:13.7584071Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:13.7627753Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:13.7660865Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:23.9823858Z Compilation time (from dynamo_timed): 9.097653525
2025-03-14T05:18:23.9825013Z pass
2025-03-14T05:18:23.9829721Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:23.9832549Z TIMING: _recursive_pre_grad_passes:0.00311 _recursive_joint_graph_passes:0.09789 _recursive_post_grad_passes:0.0165 async_compile.wait:1.32774 code_gen:7.93869 inductor_compile:8.06777 backend_compile:8.84108 entire_frame_compile:9.09765 gc:0.00073 total_wall_time:9.09765
2025-03-14T05:18:23.9833605Z STATS: call_* op count: 14 | FakeTensor.__torch_dispatch__:78 | FakeTensorMode.__torch_dispatch__:1086 | ProxyTorchDispatchMode.__torch_dispatch__:289
2025-03-14T05:18:23.9838038Z Dynamo produced 1 graphs covering 14 ops with 0 graph breaks (0 unique)
2025-03-14T05:18:28.4188402Z 
2025-03-14T05:18:30.4145887Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:18:30.4146305Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:18:30.4146582Z cpu  eval  hf_Albert                          
2025-03-14T05:18:31.9172225Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:32.8988270Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:34.7015271Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:50.3363764Z Compilation time (from dynamo_timed): 13.077482541
2025-03-14T05:18:50.3368748Z pass
2025-03-14T05:18:50.3372286Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:18:50.3373479Z TIMING: _recursive_pre_grad_passes:0.00653 _recursive_joint_graph_passes:0.64253 _recursive_post_grad_passes:0.05224 async_compile.wait:1.37654 code_gen:8.46569 inductor_compile:9.26528 backend_compile:11.70149 entire_frame_compile:13.07748 gc:0.00039 total_wall_time:13.07748
2025-03-14T05:18:50.3375768Z STATS: call_* op count: 436 | FakeTensorMode.__torch_dispatch__:12716 | FakeTensor.__torch_dispatch__:1244 | ProxyTorchDispatchMode.__torch_dispatch__:5346
2025-03-14T05:18:50.3376436Z Dynamo produced 1 graphs covering 436 ops with 0 graph breaks (0 unique)
2025-03-14T05:18:55.1085013Z 
2025-03-14T05:18:59.3016607Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:18:59.3018763Z loading model: 0it [00:04, ?it/s]
2025-03-14T05:18:59.3035954Z cpu  eval  hf_Bart                            
2025-03-14T05:19:01.7521675Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:02.7403476Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:03.6113549Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:24.2695139Z Compilation time (from dynamo_timed): 16.69551339
2025-03-14T05:19:24.2989213Z pass
2025-03-14T05:19:24.2993528Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:24.2996000Z TIMING: _recursive_pre_grad_passes:0.00765 _recursive_joint_graph_passes:0.35266 _recursive_post_grad_passes:0.06095 async_compile.wait:1.34226 code_gen:9.80185 inductor_compile:10.89535 backend_compile:14.08955 entire_frame_compile:16.69551 gc:0.00051 total_wall_time:16.69551
2025-03-14T05:19:24.3000797Z STATS: call_* op count: 451 | FakeTensorMode.__torch_dispatch__:15323 | FakeTensor.__torch_dispatch__:1515 | ProxyTorchDispatchMode.__torch_dispatch__:6179
2025-03-14T05:19:24.3005589Z Dynamo produced 1 graphs covering 451 ops with 0 graph breaks (0 unique)
2025-03-14T05:19:29.3278151Z 
2025-03-14T05:19:32.7460972Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:19:32.7462084Z loading model: 0it [00:03, ?it/s]
2025-03-14T05:19:32.7472086Z cpu  eval  hf_Bert                            
2025-03-14T05:19:36.8931346Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:38.1830276Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:39.6230731Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:56.5707632Z Compilation time (from dynamo_timed): 14.916220708
2025-03-14T05:19:56.5830102Z pass
2025-03-14T05:19:56.5836305Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:19:56.5841703Z TIMING: _recursive_pre_grad_passes:0.00693 _recursive_joint_graph_passes:0.38319 _recursive_post_grad_passes:0.04952 async_compile.wait:1.35066 code_gen:8.50346 inductor_compile:9.32329 backend_compile:12.32779 entire_frame_compile:14.91622 gc:0.00046 total_wall_time:14.91622
2025-03-14T05:19:56.5843065Z STATS: call_* op count: 367 | FakeTensorMode.__torch_dispatch__:13832 | FakeTensor.__torch_dispatch__:1141 | ProxyTorchDispatchMode.__torch_dispatch__:5845
2025-03-14T05:19:56.5843577Z Dynamo produced 1 graphs covering 367 ops with 0 graph breaks (0 unique)
2025-03-14T05:20:01.3633079Z 
2025-03-14T05:20:06.5283800Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:20:06.5284504Z loading model: 0it [00:05, ?it/s]
2025-03-14T05:20:06.5306817Z cpu  eval  hf_Bert_large                      
2025-03-14T05:20:12.5164760Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:14.2747326Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:16.0230539Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:41.1203110Z Compilation time (from dynamo_timed): 21.496985469
2025-03-14T05:20:41.1327271Z pass
2025-03-14T05:20:41.1333500Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:41.1335475Z TIMING: _recursive_pre_grad_passes:0.01101 _recursive_joint_graph_passes:0.6642 _recursive_post_grad_passes:0.08351 async_compile.wait:1.43309 code_gen:9.57457 inductor_compile:11.0849 backend_compile:16.46781 entire_frame_compile:21.49699 gc:0.00024 total_wall_time:21.49699
2025-03-14T05:20:41.1336587Z STATS: call_* op count: 715 | FakeTensorMode.__torch_dispatch__:26720 | FakeTensor.__torch_dispatch__:2209 | ProxyTorchDispatchMode.__torch_dispatch__:11437
2025-03-14T05:20:41.1340592Z Dynamo produced 1 graphs covering 715 ops with 0 graph breaks (0 unique)
2025-03-14T05:20:46.2045416Z 
2025-03-14T05:20:48.4866933Z loading model: 0it [00:00, ?it/s]Input ids are automatically padded from 819 to 832 to be a multiple of `config.block_size`: 64
2025-03-14T05:20:50.0646058Z 
2025-03-14T05:20:50.0650957Z loading model: 0it [00:03, ?it/s]
2025-03-14T05:20:50.0664747Z cpu  eval  hf_BigBird                         
2025-03-14T05:20:53.0995948Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:54.5566533Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:20:56.0124939Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:25.0240003Z W0314 05:21:25.021807 76134 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] torch._dynamo hit config.recompile_limit (8)
2025-03-14T05:21:25.0241452Z W0314 05:21:25.021807 76134 site-packages/torch/_dynamo/convert_frame.py:964] [10/8]    function: 'bigbird_block_sparse_attention' (/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/big_bird/modeling_big_bird.py:516)
2025-03-14T05:21:25.0243278Z W0314 05:21:25.021807 76134 site-packages/torch/_dynamo/convert_frame.py:964] [10/8]    last reason: 10/7: seed == 7  # np.random.seed(seed)  # transformers/models/big_bird/modeling_big_bird.py:569 in bigbird_block_sparse_attention (random.py:42 in manual_seed)
2025-03-14T05:21:25.0245178Z W0314 05:21:25.021807 76134 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] To log all recompilation reasons, use TORCH_LOGS="recompiles".
2025-03-14T05:21:25.0246410Z W0314 05:21:25.021807 76134 site-packages/torch/_dynamo/convert_frame.py:964] [10/8] To diagnose recompilation issues, see https://pytorch.org/docs/main/torch.compiler_troubleshooting.html.
2025-03-14T05:21:29.8578708Z Compilation time (from dynamo_timed): 29.210558969
2025-03-14T05:21:29.8582116Z pass
2025-03-14T05:21:29.8589465Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:29.8590850Z TIMING: entire_frame_compile:29.21056 gc:0.01916 _recursive_pre_grad_passes:0.01532 _recursive_joint_graph_passes:0.21733 _recursive_post_grad_passes:0.06949 async_compile.wait:11.3513 code_gen:23.44416 inductor_compile:24.31173 backend_compile:26.36311 total_wall_time:29.21056
2025-03-14T05:21:29.8591878Z STATS: call_* op count: 324 | FakeTensorMode.__torch_dispatch__:9150 | ProxyTorchDispatchMode.__torch_dispatch__:2992 | FakeTensor.__torch_dispatch__:698
2025-03-14T05:21:29.8592459Z Dynamo produced 22 graphs covering 324 ops with 18 graph breaks (2 unique)
2025-03-14T05:21:35.0775468Z 
2025-03-14T05:21:37.0498252Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:21:37.0498883Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:21:37.0505429Z cpu  eval  hf_DistilBert                      
2025-03-14T05:21:38.4234197Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:39.0639182Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:40.3079365Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:54.4928852Z Compilation time (from dynamo_timed): 11.950779416
2025-03-14T05:21:54.5164825Z pass
2025-03-14T05:21:54.5172759Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:21:54.5174021Z TIMING: _recursive_pre_grad_passes:0.00541 _recursive_joint_graph_passes:0.23513 _recursive_post_grad_passes:0.06207 async_compile.wait:1.23199 code_gen:8.0774 inductor_compile:8.98518 backend_compile:10.63488 entire_frame_compile:11.95078 gc:0.00103 total_wall_time:11.95078
2025-03-14T05:21:54.5178405Z STATS: call_* op count: 203 | FakeTensorMode.__torch_dispatch__:7604 | FakeTensor.__torch_dispatch__:771 | ProxyTorchDispatchMode.__torch_dispatch__:3028
2025-03-14T05:21:54.5182914Z Dynamo produced 1 graphs covering 203 ops with 0 graph breaks (0 unique)
2025-03-14T05:21:59.2790558Z 
2025-03-14T05:22:02.0644174Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:22:02.0644851Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:22:02.0653349Z cpu  eval  hf_GPT2                            
2025-03-14T05:22:03.5644785Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:03.9940402Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:04.2519224Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:20.8943268Z Compilation time (from dynamo_timed): 14.872223486
2025-03-14T05:22:20.9039322Z pass
2025-03-14T05:22:20.9040053Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:20.9041114Z TIMING: _recursive_pre_grad_passes:0.00962 _recursive_joint_graph_passes:0.36887 _recursive_post_grad_passes:0.05043 async_compile.wait:1.29766 code_gen:9.09004 inductor_compile:9.94271 backend_compile:12.796 entire_frame_compile:14.87222 gc:0.00049 total_wall_time:14.87222
2025-03-14T05:22:20.9042043Z STATS: call_* op count: 622 | FakeTensorMode.__torch_dispatch__:13270 | FakeTensor.__torch_dispatch__:1139 | ProxyTorchDispatchMode.__torch_dispatch__:5501
2025-03-14T05:22:20.9042866Z Dynamo produced 1 graphs covering 622 ops with 0 graph breaks (0 unique)
2025-03-14T05:22:25.7755026Z 
2025-03-14T05:22:53.0835898Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:22:53.0840600Z loading model: 0it [00:27, ?it/s]
2025-03-14T05:22:53.0862581Z cpu  eval  hf_GPT2_large                      
2025-03-14T05:22:53.2843659Z Compilation time (from dynamo_timed): 0
2025-03-14T05:22:53.2844525Z pass_due_to_skip
2025-03-14T05:22:53.2845185Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:22:53.2845543Z TIMING: total_wall_time:0
2025-03-14T05:22:53.2845750Z STATS: call_* op count: 0
2025-03-14T05:22:53.2852781Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T05:22:57.0982906Z 
2025-03-14T05:23:01.1872837Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:23:01.1873448Z loading model: 0it [00:04, ?it/s]
2025-03-14T05:23:01.1885693Z cpu  eval  hf_Longformer                      
2025-03-14T05:23:06.5721351Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:23:07.7784030Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:23:08.9098365Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:23:10.0716456Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:23:10.0718944Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:23:10.0719611Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] or:
2025-03-14T05:23:10.0724738Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:23:10.0725543Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] to include these operations in the captured graph.
2025-03-14T05:23:10.0726163Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:23:10.0726718Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break: from user code at:
2025-03-14T05:23:10.0727435Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 457, in forward_pass
2025-03-14T05:23:10.0728121Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     return mod(**inputs)
2025-03-14T05:23:10.0728896Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1835, in forward
2025-03-14T05:23:10.0729688Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     outputs = self.longformer(
2025-03-14T05:23:10.0730472Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1738, in forward
2025-03-14T05:23:10.0731245Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     encoder_outputs = self.encoder(
2025-03-14T05:23:10.0732014Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1291, in forward
2025-03-14T05:23:10.0732833Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     is_global_attn = is_index_global_attn.flatten().any().item()
2025-03-14T05:23:10.0733467Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:23:10.0733845Z W0314 05:23:10.069824 79236 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-14T05:24:46.0464372Z Compilation time (from dynamo_timed): 92.213407858
2025-03-14T05:24:46.0888938Z pass
2025-03-14T05:24:46.0903767Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:24:46.0910393Z TIMING: entire_frame_compile:92.21341 gc:0.00677 _recursive_pre_grad_passes:0.02016 _recursive_joint_graph_passes:0.76057 _recursive_post_grad_passes:0.70638 async_compile.wait:5.95101 code_gen:71.67415 inductor_compile:76.85193 backend_compile:87.34498 total_wall_time:92.21341
2025-03-14T05:24:46.0918207Z STATS: call_* op count: 1783 | FakeTensorMode.__torch_dispatch__:57486 | FakeTensor.__torch_dispatch__:4861 | ProxyTorchDispatchMode.__torch_dispatch__:21836
2025-03-14T05:24:46.0922820Z Dynamo produced 4 graphs covering 1783 ops with 4 graph breaks (1 unique)
2025-03-14T05:24:52.0600229Z 
2025-03-14T05:24:53.7124272Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:24:53.7124748Z loading model: 0it [00:01, ?it/s]
2025-03-14T05:24:53.7128026Z cpu  eval  hf_Reformer                        
2025-03-14T05:24:56.7230658Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:24:58.0659646Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:24:58.6931399Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:24:59.6962483Z 
2025-03-14T05:24:59.6966130Z class GraphModule(torch.nn.Module):
2025-03-14T05:24:59.6968822Z     def forward(self, L_inputs_input_ids_: "i64[4, 2048][2048, 1]cpu"):
2025-03-14T05:24:59.6973287Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-14T05:24:59.6976003Z         
2025-03-14T05:24:59.6982339Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:24:59.6983260Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:24:59.6983558Z         
2025-03-14T05:24:59.6984064Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:24:59.6984885Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:24:59.6985406Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:24:59.6985740Z         
2025-03-14T05:24:59.6985834Z 
2025-03-14T05:24:59.6985940Z class GraphModule(torch.nn.Module):
2025-03-14T05:24:59.6986241Z     def forward(self, L_inputs_input_ids_: "i64[4, 2048][2048, 1]cpu"):
2025-03-14T05:24:59.6986528Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-14T05:24:59.6986752Z         
2025-03-14T05:24:59.6987312Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:24:59.6987991Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:24:59.6988276Z         
2025-03-14T05:24:59.6988750Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:24:59.6989527Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:24:59.6990026Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:24:59.6990351Z         
2025-03-14T05:24:59.7379342Z 
2025-03-14T05:24:59.7382929Z class GraphModule(torch.nn.Module):
2025-03-14T05:24:59.7383482Z     def forward(self, L_input_ids_: "i64[4, 2048][2048, 1]cpu"):
2025-03-14T05:24:59.7383921Z         l_input_ids_ = L_input_ids_
2025-03-14T05:24:59.7384344Z         
2025-03-14T05:24:59.7385841Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-14T05:24:59.7387133Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-14T05:24:59.7387632Z         
2025-03-14T05:24:59.7388458Z          # File: /opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-14T05:24:59.7389663Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(2048, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-14T05:24:59.7390535Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-14T05:24:59.7391063Z         
2025-03-14T05:25:07.1876792Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break from `Tensor.item()`, consider setting:
2025-03-14T05:25:07.1877591Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-14T05:25:07.1878203Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] or:
2025-03-14T05:25:07.1879114Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-14T05:25:07.1879782Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] to include these operations in the captured graph.
2025-03-14T05:25:07.1880408Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:25:07.1880904Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break: from user code at:
2025-03-14T05:25:07.1881963Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 2094, in torch_dynamo_resume_in_forward_at_2066
2025-03-14T05:25:07.1882891Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     embedding_output = self.embeddings(
2025-03-14T05:25:07.1883707Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 266, in forward
2025-03-14T05:25:07.1884547Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     position_embeddings = self.position_embeddings(position_ids)
2025-03-14T05:25:07.1885397Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.9/lib/python3.9/site-packages/transformers/models/reformer/modeling_reformer.py", line 190, in forward
2025-03-14T05:25:07.1886176Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     max_position_id = position_ids.max().item()
2025-03-14T05:25:07.1886661Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:25:07.1887103Z W0314 05:25:07.185681 80296 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-14T05:25:22.3296496Z Compilation time (from dynamo_timed): 22.218207257
2025-03-14T05:25:22.3297788Z pass
2025-03-14T05:25:22.3298777Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:25:22.3300086Z TIMING: entire_frame_compile:22.21821 gc:0.00842 _recursive_pre_grad_passes:0.01286 _recursive_joint_graph_passes:0.27707 _recursive_post_grad_passes:0.05634 async_compile.wait:7.61085 code_gen:16.47536 inductor_compile:17.93346 backend_compile:19.76128 total_wall_time:22.21821
2025-03-14T05:25:22.3301133Z STATS: call_* op count: 482 | FakeTensorMode.__torch_dispatch__:12133 | ProxyTorchDispatchMode.__torch_dispatch__:4614 | FakeTensor.__torch_dispatch__:1103
2025-03-14T05:25:22.3301751Z Dynamo produced 7 graphs covering 482 ops with 5 graph breaks (2 unique)
2025-03-14T05:25:27.2841895Z 
2025-03-14T05:25:32.9106004Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:25:32.9110425Z loading model: 0it [00:05, ?it/s]
2025-03-14T05:25:32.9118636Z cpu  eval  hf_Roberta_base                    
2025-03-14T05:25:37.6664782Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:25:40.7219308Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:25:44.0858660Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:05.6715693Z Compilation time (from dynamo_timed): 15.747752285
2025-03-14T05:26:05.8538019Z pass
2025-03-14T05:26:05.8540253Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:05.8545583Z TIMING: _recursive_pre_grad_passes:0.00693 _recursive_joint_graph_passes:0.40386 _recursive_post_grad_passes:0.05078 async_compile.wait:1.38618 code_gen:9.33826 inductor_compile:10.16943 backend_compile:13.20283 entire_frame_compile:15.74775 gc:9e-05 total_wall_time:15.74775
2025-03-14T05:26:05.8546911Z STATS: call_* op count: 374 | FakeTensorMode.__torch_dispatch__:13932 | FakeTensor.__torch_dispatch__:1159 | ProxyTorchDispatchMode.__torch_dispatch__:5884
2025-03-14T05:26:05.8547431Z Dynamo produced 1 graphs covering 374 ops with 0 graph breaks (0 unique)
2025-03-14T05:26:10.8992838Z 
2025-03-14T05:26:13.1443189Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:26:13.1443503Z loading model: 0it [00:02, ?it/s]
2025-03-14T05:26:13.1454345Z cpu  eval  hf_T5                              
2025-03-14T05:26:14.9877236Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:15.5325714Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:16.2472079Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:36.8596572Z Compilation time (from dynamo_timed): 18.440036941
2025-03-14T05:26:36.8889087Z pass
2025-03-14T05:26:36.8893272Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:26:36.8895380Z TIMING: _recursive_pre_grad_passes:0.01082 _recursive_joint_graph_passes:0.43762 _recursive_post_grad_passes:0.13409 async_compile.wait:1.66712 code_gen:11.15587 inductor_compile:12.46593 backend_compile:15.89254 entire_frame_compile:18.44004 gc:0.00081 total_wall_time:18.44004
2025-03-14T05:26:36.8896467Z STATS: call_* op count: 795 | FakeTensorMode.__torch_dispatch__:18938 | ProxyTorchDispatchMode.__torch_dispatch__:7574 | FakeTensor.__torch_dispatch__:1590
2025-03-14T05:26:36.8896975Z Dynamo produced 1 graphs covering 795 ops with 0 graph breaks (0 unique)
2025-03-14T05:26:41.8975773Z 
2025-03-14T05:26:56.9330729Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:26:56.9331046Z loading model: 0it [00:15, ?it/s]
2025-03-14T05:26:56.9353139Z cpu  eval  hf_T5_base                         
2025-03-14T05:27:34.3224708Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:27:52.8956886Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:28:08.2030064Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:28:56.2817650Z Compilation time (from dynamo_timed): 30.152354588
2025-03-14T05:28:56.3222924Z pass
2025-03-14T05:28:56.3226667Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:28:56.3231338Z TIMING: _recursive_pre_grad_passes:0.01832 _recursive_joint_graph_passes:0.79204 _recursive_post_grad_passes:0.25306 async_compile.wait:1.48479 code_gen:15.29444 inductor_compile:18.60876 backend_compile:24.88736 entire_frame_compile:30.15235 gc:0.00042 total_wall_time:30.15235
2025-03-14T05:28:56.3232387Z STATS: call_* op count: 1491 | FakeTensorMode.__torch_dispatch__:36116 | ProxyTorchDispatchMode.__torch_dispatch__:14510 | FakeTensor.__torch_dispatch__:3036
2025-03-14T05:28:56.3233011Z Dynamo produced 1 graphs covering 1491 ops with 0 graph breaks (0 unique)
2025-03-14T05:29:02.2896591Z 
2025-03-14T05:29:15.7438115Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:29:15.7439984Z loading model: 0it [00:13, ?it/s]
2025-03-14T05:29:15.7473280Z cpu  eval  hf_T5_large                        
2025-03-14T05:29:15.9446028Z Compilation time (from dynamo_timed): 0
2025-03-14T05:29:15.9446720Z pass_due_to_skip
2025-03-14T05:29:15.9447736Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:29:15.9448176Z TIMING: total_wall_time:0
2025-03-14T05:29:15.9448415Z STATS: call_* op count: 0
2025-03-14T05:29:15.9448723Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-14T05:29:19.6371144Z 
2025-03-14T05:29:26.7758477Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:29:26.7763485Z loading model: 0it [00:07, ?it/s]
2025-03-14T05:29:26.7789852Z cpu  eval  hf_distil_whisper                  
2025-03-14T05:29:39.6537719Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:29:47.7281638Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:29:53.6845389Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:23.0547299Z Compilation time (from dynamo_timed): 21.551579893
2025-03-14T05:30:23.0548662Z pass
2025-03-14T05:30:23.0554787Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:23.0556994Z TIMING: _recursive_pre_grad_passes:0.00969 _recursive_joint_graph_passes:0.48144 _recursive_post_grad_passes:0.38935 async_compile.wait:1.47744 code_gen:11.38779 inductor_compile:13.36533 backend_compile:17.96187 entire_frame_compile:21.55158 gc:0.00092 total_wall_time:21.55158
2025-03-14T05:30:23.0558576Z STATS: call_* op count: 635 | FakeTensorMode.__torch_dispatch__:22259 | FakeTensor.__torch_dispatch__:2316 | ProxyTorchDispatchMode.__torch_dispatch__:9142
2025-03-14T05:30:23.0560809Z Dynamo produced 1 graphs covering 635 ops with 0 graph breaks (0 unique)
2025-03-14T05:30:28.1937112Z 
2025-03-14T05:30:28.3508446Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:30:28.3513144Z loading model: 0it [00:00, ?it/s]
2025-03-14T05:30:28.3517778Z cpu  eval  lennard_jones                      
2025-03-14T05:30:28.3523047Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:28.3532394Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:28.3537810Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:36.6677393Z Compilation time (from dynamo_timed): 7.395875506
2025-03-14T05:30:36.6678088Z pass
2025-03-14T05:30:36.6678445Z WARNING:common:Trying to call the empty_gpu_cache for device: cpu, which is not in list [cuda, xpu]
2025-03-14T05:30:36.6679357Z TIMING: _recursive_pre_grad_passes:0.00291 _recursive_joint_graph_passes:0.09525 _recursive_post_grad_passes:0.01437 async_compile.wait:1.21568 code_gen:6.56136 inductor_compile:6.61526 backend_compile:7.26116 entire_frame_compile:7.39588 gc:0.00072 total_wall_time:7.39588
2025-03-14T05:30:36.6680372Z STATS: call_* op count: 9 | FakeTensor.__torch_dispatch__:25 | FakeTensorMode.__torch_dispatch__:686 | ProxyTorchDispatchMode.__torch_dispatch__:176
2025-03-14T05:30:36.6681061Z Dynamo produced 1 graphs covering 9 ops with 0 graph breaks (0 unique)
2025-03-14T05:30:38.3773766Z accuracy            pass_rate=91.30%
2025-03-14T05:30:38.3778663Z calls_captured      gmean=0.00x mean=464.043x
2025-03-14T05:30:38.3779997Z unique_graphs       gmean=0.00x mean=15.826x
2025-03-14T05:30:38.3780361Z graph_breaks        gmean=0.00x mean=12.326x
2025-03-14T05:30:38.3780797Z unique_graph_breaks gmean=0.00x mean=1.848x
2025-03-14T05:30:38.3781050Z autograd_captures   gmean=0.00x mean=0.000x
2025-03-14T05:30:38.3781394Z autograd_compiles   gmean=0.00x mean=0.000x
2025-03-14T05:30:38.3790570Z cudagraph_skips     gmean=0.00x mean=11.000x
2025-03-14T05:30:38.3790945Z compilation_latency mean=25.591 seconds
2025-03-14T05:30:39.1208019Z + python benchmarks/dynamo/check_accuracy.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_torchbench_inference.csv
2025-03-14T05:30:39.4412071Z BERT_pytorch                        PASS
2025-03-14T05:30:39.4416307Z Background_Matting                  XFAIL
2025-03-14T05:30:39.4422054Z LearningToPaint                     PASS
2025-03-14T05:30:39.4422333Z Super_SloMo                         PASS
2025-03-14T05:30:39.4422562Z alexnet                             PASS
2025-03-14T05:30:39.4429105Z basic_gnn_edgecnn                   PASS
2025-03-14T05:30:39.4431361Z basic_gnn_gcn                       PASS
2025-03-14T05:30:39.4440777Z basic_gnn_gin                       PASS
2025-03-14T05:30:39.4444943Z basic_gnn_sage                      PASS
2025-03-14T05:30:39.4446854Z dcgan                               PASS
2025-03-14T05:30:39.4447124Z demucs                              PASS
2025-03-14T05:30:39.4447729Z densenet121                         PASS
2025-03-14T05:30:39.4448046Z detectron2_fasterrcnn_r_101_c4      PASS
2025-03-14T05:30:39.4454667Z detectron2_fasterrcnn_r_101_dc5     PASS
2025-03-14T05:30:39.4459041Z detectron2_fasterrcnn_r_101_fpn     PASS
2025-03-14T05:30:39.4463527Z detectron2_fasterrcnn_r_50_c4       PASS
2025-03-14T05:30:39.4465351Z detectron2_fasterrcnn_r_50_dc5      PASS
2025-03-14T05:30:39.4465687Z detectron2_fasterrcnn_r_50_fpn      PASS
2025-03-14T05:30:39.4465979Z detectron2_fcos_r_50_fpn            PASS
2025-03-14T05:30:39.4475379Z detectron2_maskrcnn_r_101_c4        XFAIL
2025-03-14T05:30:39.4479593Z detectron2_maskrcnn_r_101_fpn       PASS
2025-03-14T05:30:39.4481331Z detectron2_maskrcnn_r_50_c4         PASS
2025-03-14T05:30:39.4481956Z detectron2_maskrcnn_r_50_fpn        PASS
2025-03-14T05:30:39.4482305Z dlrm                                PASS
2025-03-14T05:30:39.4487007Z doctr_det_predictor                 PASS
2025-03-14T05:30:39.4497236Z doctr_reco_predictor                PASS
2025-03-14T05:30:39.4501852Z drq                                 PASS
2025-03-14T05:30:39.4502160Z fastNLP_Bert                        PASS
2025-03-14T05:30:39.4502467Z functorch_dp_cifar10                PASS
2025-03-14T05:30:39.4502720Z functorch_maml_omniglot             PASS
2025-03-14T05:30:39.4503120Z hf_Albert                           PASS
2025-03-14T05:30:39.4515512Z hf_Bart                             PASS
2025-03-14T05:30:39.4515812Z hf_Bert                             PASS
2025-03-14T05:30:39.4516247Z hf_Bert_large                       PASS
2025-03-14T05:30:39.4523343Z hf_BigBird                          PASS
2025-03-14T05:30:39.4526776Z hf_DistilBert                       PASS
2025-03-14T05:30:39.4527411Z hf_GPT2                             PASS
2025-03-14T05:30:39.4533144Z hf_GPT2_large                       XFAIL
2025-03-14T05:30:39.4539079Z hf_Longformer                       PASS
2025-03-14T05:30:39.4539392Z hf_Reformer                         PASS
2025-03-14T05:30:39.4541394Z hf_Roberta_base                     PASS
2025-03-14T05:30:39.4541908Z hf_T5                               PASS
2025-03-14T05:30:39.4549273Z hf_T5_base                          PASS
2025-03-14T05:30:39.4552405Z hf_T5_large                         XFAIL
2025-03-14T05:30:39.4556911Z hf_distil_whisper                   PASS
2025-03-14T05:30:39.4561745Z lennard_jones                       PASS
2025-03-14T05:30:39.5132045Z + python benchmarks/dynamo/check_graph_breaks.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/cpu_inductor_torchbench_inference.csv
2025-03-14T05:30:39.7990928Z BERT_pytorch                        PASS
2025-03-14T05:30:39.7995725Z Background_Matting                  PASS
2025-03-14T05:30:39.8002002Z LearningToPaint                     PASS
2025-03-14T05:30:39.8004017Z Super_SloMo                         PASS
2025-03-14T05:30:39.8004400Z alexnet                             PASS
2025-03-14T05:30:39.8010956Z basic_gnn_edgecnn                   PASS
2025-03-14T05:30:39.8012977Z basic_gnn_gcn                       PASS
2025-03-14T05:30:39.8013349Z basic_gnn_gin                       PASS
2025-03-14T05:30:39.8019338Z basic_gnn_sage                      PASS
2025-03-14T05:30:39.8021989Z dcgan                               PASS
2025-03-14T05:30:39.8022366Z demucs                              PASS
2025-03-14T05:30:39.8025938Z densenet121                         PASS
2025-03-14T05:30:39.8030762Z detectron2_fasterrcnn_r_101_c4      PASS
2025-03-14T05:30:39.8035566Z detectron2_fasterrcnn_r_101_dc5     PASS
2025-03-14T05:30:39.8040955Z detectron2_fasterrcnn_r_101_fpn     PASS
2025-03-14T05:30:39.8043162Z detectron2_fasterrcnn_r_50_c4       PASS
2025-03-14T05:30:39.8049588Z detectron2_fasterrcnn_r_50_dc5      PASS
2025-03-14T05:30:39.8051174Z detectron2_fasterrcnn_r_50_fpn      PASS
2025-03-14T05:30:39.8051564Z detectron2_fcos_r_50_fpn            PASS
2025-03-14T05:30:39.8057145Z detectron2_maskrcnn_r_101_c4        PASS_BUT_FLAKY
2025-03-14T05:30:39.8059321Z detectron2_maskrcnn_r_101_fpn       PASS
2025-03-14T05:30:39.8059700Z detectron2_maskrcnn_r_50_c4         PASS
2025-03-14T05:30:39.8063545Z detectron2_maskrcnn_r_50_fpn        PASS
2025-03-14T05:30:39.8063845Z dlrm                                PASS
2025-03-14T05:30:39.8064182Z doctr_det_predictor                 PASS
2025-03-14T05:30:39.8064426Z doctr_reco_predictor                PASS
2025-03-14T05:30:39.8064654Z drq                                 PASS
2025-03-14T05:30:39.8064872Z fastNLP_Bert                        PASS
2025-03-14T05:30:39.8067378Z functorch_dp_cifar10                PASS
2025-03-14T05:30:39.8074183Z functorch_maml_omniglot             PASS
2025-03-14T05:30:39.8081213Z hf_Albert                           PASS
2025-03-14T05:30:39.8086027Z hf_Bart                             PASS
2025-03-14T05:30:39.8090086Z hf_Bert                             PASS
2025-03-14T05:30:39.8092272Z hf_Bert_large                       PASS
2025-03-14T05:30:39.8092572Z hf_BigBird                          PASS
2025-03-14T05:30:39.8092795Z hf_DistilBert                       PASS
2025-03-14T05:30:39.8093017Z hf_GPT2                             PASS
2025-03-14T05:30:39.8093231Z hf_GPT2_large                       PASS
2025-03-14T05:30:39.8100882Z hf_Longformer                       PASS
2025-03-14T05:30:39.8105061Z hf_Reformer                         PASS
2025-03-14T05:30:39.8109906Z hf_Roberta_base                     PASS
2025-03-14T05:30:39.8114095Z hf_T5                               PASS
2025-03-14T05:30:39.8118141Z hf_T5_base                          PASS
2025-03-14T05:30:39.8118427Z hf_T5_large                         PASS
2025-03-14T05:30:39.8118927Z hf_distil_whisper                   PASS
2025-03-14T05:30:39.8119594Z lennard_jones                       PASS
2025-03-14T05:30:39.8647529Z + cleanup_workspace
2025-03-14T05:30:39.8649409Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2025-03-14T05:30:39.8650035Z sudo may print the following warning message that can be ignored. The chown command will still run.
2025-03-14T05:30:39.8654539Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2025-03-14T05:30:39.8656343Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2025-03-14T05:30:39.8657205Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2025-03-14T05:30:39.8657740Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2025-03-14T05:30:39.8658219Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2025-03-14T05:30:40.2327335Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-03-14T05:30:40.2327665Z with:
2025-03-14T05:30:40.2327868Z   benchmark-results-dir: test/test-reports
2025-03-14T05:30:40.2328144Z   dry-run: false
2025-03-14T05:30:40.2328321Z   schema-version: v3
2025-03-14T05:30:40.2328764Z   github-token: ***
2025-03-14T05:30:40.2328942Z env:
2025-03-14T05:30:40.2329110Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:40.2329418Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:40.2329759Z ##[endgroup]
2025-03-14T05:30:40.2357559Z ##[group]Run set -eux
2025-03-14T05:30:40.2357785Z set -eux
2025-03-14T05:30:40.2357999Z python3 -mpip install boto3==1.35.33
2025-03-14T05:30:40.2365632Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:40.2365895Z env:
2025-03-14T05:30:40.2366069Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:40.2366413Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:40.2366721Z ##[endgroup]
2025-03-14T05:30:40.2390143Z + python3 -mpip install boto3==1.35.33
2025-03-14T05:30:40.4329362Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T05:30:41.1579400Z Collecting boto3==1.35.33
2025-03-14T05:30:41.1625357Z   Using cached boto3-1.35.33-py3-none-any.whl (139 kB)
2025-03-14T05:30:41.1641775Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0)
2025-03-14T05:30:41.1648420Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.4)
2025-03-14T05:30:41.1653185Z Requirement already satisfied: botocore<1.36.0,>=1.35.33 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.33) (1.35.99)
2025-03-14T05:30:41.1692586Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.2)
2025-03-14T05:30:41.1697809Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10)
2025-03-14T05:30:41.1727704Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0)
2025-03-14T05:30:41.2919122Z Installing collected packages: boto3
2025-03-14T05:30:41.2924648Z   Attempting uninstall: boto3
2025-03-14T05:30:41.2928701Z     Found existing installation: boto3 1.35.42
2025-03-14T05:30:41.2983244Z     Uninstalling boto3-1.35.42:
2025-03-14T05:30:41.2991188Z       Successfully uninstalled boto3-1.35.42
2025-03-14T05:30:41.3430741Z Successfully installed boto3-1.35.33
2025-03-14T05:30:41.4214381Z ##[group]Run set -eux
2025-03-14T05:30:41.4214608Z set -eux
2025-03-14T05:30:41.4214778Z 
2025-03-14T05:30:41.4214980Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-03-14T05:30:41.4215221Z   echo "Missing github-token input"
2025-03-14T05:30:41.4215498Z   exit 1
2025-03-14T05:30:41.4215653Z fi
2025-03-14T05:30:41.4220036Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:41.4220289Z env:
2025-03-14T05:30:41.4220466Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:41.4220758Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:41.4221300Z   GITHUB_TOKEN: ***
2025-03-14T05:30:41.4221475Z ##[endgroup]
2025-03-14T05:30:41.4244013Z + [[ -z *** ]]
2025-03-14T05:30:41.4286388Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-03-14T05:30:41.4286678Z with:
2025-03-14T05:30:41.4286977Z   github-token: ***
2025-03-14T05:30:41.4287148Z env:
2025-03-14T05:30:41.4287304Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:41.4287743Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:41.4288101Z ##[endgroup]
2025-03-14T05:30:41.4310791Z ##[group]Run set -eux
2025-03-14T05:30:41.4311011Z set -eux
2025-03-14T05:30:41.4311181Z 
2025-03-14T05:30:41.4311486Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-14T05:30:41.4333590Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:41.4333865Z env:
2025-03-14T05:30:41.4334036Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:41.4334343Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:41.4334877Z   GITHUB_TOKEN: ***
2025-03-14T05:30:41.4335064Z ##[endgroup]
2025-03-14T05:30:41.4358222Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13849515380 i-047a1559c2de50868
2025-03-14T05:30:42.2790647Z setting job-id=38754841598
2025-03-14T05:30:42.2794870Z setting job-name=linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:30:42.2900630Z ##[group]Run set -eux
2025-03-14T05:30:42.2900939Z set -eux
2025-03-14T05:30:42.2901124Z 
2025-03-14T05:30:42.2901388Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-03-14T05:30:42.2901708Z   --schema-version "${SCHEMA_VERSION}" \
2025-03-14T05:30:42.2902058Z   --repo "${REPO}" \
2025-03-14T05:30:42.2902278Z   --head-branch "${HEAD_BRANCH}" \
2025-03-14T05:30:42.2902520Z   --head-sha "${HEAD_SHA}" \
2025-03-14T05:30:42.2902749Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-03-14T05:30:42.2902983Z   --run-attempt "${RUN_ATTEMPT}" \
2025-03-14T05:30:42.2903196Z   --job-id "${JOB_ID}" \
2025-03-14T05:30:42.2903406Z   --job-name "${JOB_NAME}"
2025-03-14T05:30:42.2907817Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.2908076Z env:
2025-03-14T05:30:42.2908255Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.2908570Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.2908887Z   SCHEMA_VERSION: v3
2025-03-14T05:30:42.2909077Z   REPO: pytorch/pytorch
2025-03-14T05:30:42.2909317Z   HEAD_BRANCH: refs/heads/main
2025-03-14T05:30:42.2909551Z   HEAD_SHA: aed0b7a742a2d7b7901790622829cbd2135049a4
2025-03-14T05:30:42.2909778Z   WORKFLOW_RUN_ID: 13849515380
2025-03-14T05:30:42.2909976Z   RUN_ATTEMPT: 1
2025-03-14T05:30:42.2910153Z   JOB_ID: 38754841598
2025-03-14T05:30:42.2910486Z   JOB_NAME: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:30:42.2910842Z ##[endgroup]
2025-03-14T05:30:42.2934127Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha aed0b7a742a2d7b7901790622829cbd2135049a4 --workflow-id 13849515380 --run-attempt 1 --job-id 38754841598 --job-name 'linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)'
2025-03-14T05:30:42.3194262Z ##[group]Run set -eux
2025-03-14T05:30:42.3194485Z set -eux
2025-03-14T05:30:42.3194675Z 
2025-03-14T05:30:42.3194884Z # TODO (huydhn): Implement this part
2025-03-14T05:30:42.3195137Z echo "runners=[]" >> "${GITHUB_OUTPUT}"
2025-03-14T05:30:42.3199613Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.3199869Z env:
2025-03-14T05:30:42.3200046Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.3200345Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.3200655Z ##[endgroup]
2025-03-14T05:30:42.3223479Z + echo 'runners=[]'
2025-03-14T05:30:42.3254724Z ##[group]Run set -eux
2025-03-14T05:30:42.3255066Z set -eux
2025-03-14T05:30:42.3255237Z 
2025-03-14T05:30:42.3255429Z # TODO (huydhn): Implement this part
2025-03-14T05:30:42.3255695Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-03-14T05:30:42.3259916Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.3260168Z env:
2025-03-14T05:30:42.3260338Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.3260643Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.3260955Z ##[endgroup]
2025-03-14T05:30:42.3280701Z + echo 'dependencies={}'
2025-03-14T05:30:42.3307834Z ##[group]Run set -eux
2025-03-14T05:30:42.3308053Z set -eux
2025-03-14T05:30:42.3308221Z 
2025-03-14T05:30:42.3308416Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-03-14T05:30:42.3308698Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-03-14T05:30:42.3309015Z   # We don't want the job to fail if the directory doesn't exist
2025-03-14T05:30:42.3309263Z   exit 0
2025-03-14T05:30:42.3309429Z fi
2025-03-14T05:30:42.3309598Z 
2025-03-14T05:30:42.3309773Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-03-14T05:30:42.3310077Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T05:30:42.3310419Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T05:30:42.3310778Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T05:30:42.3311012Z     --runners "${RUNNER_INFO}" \
2025-03-14T05:30:42.3311244Z     --dependencies "${DEPENDENCIES}" \
2025-03-14T05:30:42.3311466Z     --dry-run
2025-03-14T05:30:42.3311649Z else
2025-03-14T05:30:42.3311890Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-14T05:30:42.3312225Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-14T05:30:42.3312496Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-14T05:30:42.3312725Z     --runners "${RUNNER_INFO}" \
2025-03-14T05:30:42.3312952Z     --dependencies "${DEPENDENCIES}"
2025-03-14T05:30:42.3313163Z fi
2025-03-14T05:30:42.3316750Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.3316996Z env:
2025-03-14T05:30:42.3317161Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.3317463Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.3317791Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-03-14T05:30:42.3318009Z   DRY_RUN: false
2025-03-14T05:30:42.3318796Z   BENCHMARK_METADATA: {"timestamp": 1741930242, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515380, "run_attempt": 1, "job_id": 38754841598}
2025-03-14T05:30:42.3319587Z   RUNNER_INFO: []
2025-03-14T05:30:42.3319765Z   DEPENDENCIES: {}
2025-03-14T05:30:42.3319981Z ##[endgroup]
2025-03-14T05:30:42.3338395Z + [[ ! -d test/test-reports ]]
2025-03-14T05:30:42.3339458Z + [[ false == \t\r\u\e ]]
2025-03-14T05:30:42.3340833Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1741930242, "schema_version": "v3", "name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "aed0b7a742a2d7b7901790622829cbd2135049a4", "workflow_id": 13849515380, "run_attempt": 1, "job_id": 38754841598}' --runners '[]' --dependencies '{}'
2025-03-14T05:30:42.4499427Z INFO:root:Upload test/test-reports/inference_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13849515380/38754841598/inference_torchbench.json
2025-03-14T05:30:42.4772855Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role
2025-03-14T05:30:42.6926635Z INFO:root:Upload test/test-reports/inference_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13849515380/38754841598/inference_torchbench_graph_breaks.json
2025-03-14T05:30:42.9148637Z ##[group]Run cat test/**/*_toprint.log || true
2025-03-14T05:30:42.9148926Z cat test/**/*_toprint.log || true
2025-03-14T05:30:42.9153323Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.9153581Z env:
2025-03-14T05:30:42.9153754Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.9154053Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.9154358Z ##[endgroup]
2025-03-14T05:30:42.9250352Z cat: 'test/**/*_toprint.log': No such file or directory
2025-03-14T05:30:42.9284966Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2025-03-14T05:30:42.9285228Z kill "$MONITOR_SCRIPT_PID"
2025-03-14T05:30:42.9289540Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:42.9289803Z env:
2025-03-14T05:30:42.9289973Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:42.9290263Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:42.9290581Z   MONITOR_SCRIPT_PID: 227980
2025-03-14T05:30:42.9290840Z ##[endgroup]
2025-03-14T05:30:42.9404837Z Prepare all required actions
2025-03-14T05:30:42.9405233Z Getting action download info
2025-03-14T05:30:43.0465707Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1)
2025-03-14T05:30:43.5230989Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-03-14T05:30:43.5231263Z with:
2025-03-14T05:30:43.5231545Z   file-suffix: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T05:30:43.5231853Z   s3-bucket: gha-artifacts
2025-03-14T05:30:43.5232057Z env:
2025-03-14T05:30:43.5232229Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5232544Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5232890Z ##[endgroup]
2025-03-14T05:30:43.5257142Z ##[group]Run # Remove any previous test jsons if they exist
2025-03-14T05:30:43.5257463Z # Remove any previous test jsons if they exist
2025-03-14T05:30:43.5257734Z rm -f test-jsons-*.zip
2025-03-14T05:30:43.5258058Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2025-03-14T05:30:43.5262313Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:43.5262569Z env:
2025-03-14T05:30:43.5262751Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5263063Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5263466Z   FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T05:30:43.5263755Z ##[endgroup]
2025-03-14T05:30:43.5394775Z   adding: test/test-reports/inference_torchbench.json (deflated 99%)
2025-03-14T05:30:43.5616449Z   adding: test/test-reports/inference_torchbench_graph_breaks.json (deflated 99%)
2025-03-14T05:30:43.5647064Z ##[group]Run # Remove any previous test reports if they exist
2025-03-14T05:30:43.5647402Z # Remove any previous test reports if they exist
2025-03-14T05:30:43.5647667Z rm -f test-reports-*.zip
2025-03-14T05:30:43.5648004Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2025-03-14T05:30:43.5652458Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:43.5652719Z env:
2025-03-14T05:30:43.5652891Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5653192Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5653590Z   FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T05:30:43.5653873Z ##[endgroup]
2025-03-14T05:30:43.5699449Z   adding: test/test-reports/inference_torchbench.csv (deflated 63%)
2025-03-14T05:30:43.5724054Z   adding: test/test-reports/inference_torchbench_graph_breaks.csv (deflated 97%)
2025-03-14T05:30:43.5728613Z   adding: test/test-reports/inference_torchbench_graph_break_deduped.csv (deflated 80%)
2025-03-14T05:30:43.5756118Z ##[group]Run # Remove any previous usage logs if they exist
2025-03-14T05:30:43.5756479Z # Remove any previous usage logs if they exist
2025-03-14T05:30:43.5756735Z rm -f logs-*.zip
2025-03-14T05:30:43.5757039Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2025-03-14T05:30:43.5757371Z # so check to see if the file exists first
2025-03-14T05:30:43.5757617Z if [ -f 'usage_log.txt' ]; then
2025-03-14T05:30:43.5757872Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2025-03-14T05:30:43.5758110Z fi
2025-03-14T05:30:43.5758365Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2025-03-14T05:30:43.5758708Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2025-03-14T05:30:43.5758963Z fi
2025-03-14T05:30:43.5763217Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:43.5763470Z env:
2025-03-14T05:30:43.5763645Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5764079Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5764533Z   FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T05:30:43.5764819Z ##[endgroup]
2025-03-14T05:30:43.5827648Z   adding: usage_log.txt (deflated 96%)
2025-03-14T05:30:43.5872118Z ##[group]Run # Remove any previous debugging artifacts if they exist
2025-03-14T05:30:43.5872488Z # Remove any previous debugging artifacts if they exist
2025-03-14T05:30:43.5872765Z rm -f debug-*.zip
2025-03-14T05:30:43.5872982Z if [ -d 'test/debug' ]; then
2025-03-14T05:30:43.5873243Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2025-03-14T05:30:43.5873509Z fi
2025-03-14T05:30:43.5877755Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:43.5878018Z env:
2025-03-14T05:30:43.5878196Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5878507Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5878945Z   FILE_SUFFIX: test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598
2025-03-14T05:30:43.5879236Z ##[endgroup]
2025-03-14T05:30:43.5971555Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:30:43.5971795Z with:
2025-03-14T05:30:43.5971974Z   s3-bucket: gha-artifacts
2025-03-14T05:30:43.5972204Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:30:43.5972436Z   retention-days: 14
2025-03-14T05:30:43.5972619Z   if-no-files-found: warn
2025-03-14T05:30:43.5972813Z   path: test-jsons-*.zip
2025-03-14T05:30:43.5972994Z   name: artifact
2025-03-14T05:30:43.5973156Z   region: us-east-1
2025-03-14T05:30:43.5973326Z env:
2025-03-14T05:30:43.5973559Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:43.5973856Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:43.5974155Z ##[endgroup]
2025-03-14T05:30:43.8752603Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:30:43.8758927Z With the provided path, there will be 1 file uploaded
2025-03-14T05:30:43.8760948Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:30:43.8776740Z Starting upload of test-jsons-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.0486479Z Finished upload of test-jsons-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.0670241Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:30:44.0670487Z with:
2025-03-14T05:30:44.0670668Z   s3-bucket: gha-artifacts
2025-03-14T05:30:44.0670901Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:30:44.0671131Z   retention-days: 14
2025-03-14T05:30:44.0671333Z   if-no-files-found: error
2025-03-14T05:30:44.0671529Z   path: test-reports-*.zip
2025-03-14T05:30:44.0671712Z   name: artifact
2025-03-14T05:30:44.0671876Z   region: us-east-1
2025-03-14T05:30:44.0672045Z env:
2025-03-14T05:30:44.0672206Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:44.0672522Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:44.0672838Z ##[endgroup]
2025-03-14T05:30:44.3207449Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:30:44.3207964Z With the provided path, there will be 1 file uploaded
2025-03-14T05:30:44.3208350Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:30:44.3232139Z Starting upload of test-reports-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.4664593Z Finished upload of test-reports-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.4836228Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:30:44.4836486Z with:
2025-03-14T05:30:44.4836662Z   s3-bucket: gha-artifacts
2025-03-14T05:30:44.4836889Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:30:44.4837124Z   retention-days: 14
2025-03-14T05:30:44.4837309Z   if-no-files-found: ignore
2025-03-14T05:30:44.4837503Z   path: logs-*.zip
2025-03-14T05:30:44.4837867Z   name: artifact
2025-03-14T05:30:44.4838038Z   region: us-east-1
2025-03-14T05:30:44.4838214Z env:
2025-03-14T05:30:44.4838386Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:44.4838697Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:44.4839004Z ##[endgroup]
2025-03-14T05:30:44.7443520Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-14T05:30:44.7447891Z With the provided path, there will be 1 file uploaded
2025-03-14T05:30:44.7450176Z Uploading to s3 prefix: pytorch/pytorch/13849515380/1/artifact
2025-03-14T05:30:44.7468513Z Starting upload of logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.8952496Z Finished upload of logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:44.9120533Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-14T05:30:44.9120778Z with:
2025-03-14T05:30:44.9120958Z   s3-bucket: gha-artifacts
2025-03-14T05:30:44.9121220Z   s3-prefix: pytorch/pytorch/13849515380/1/artifact

2025-03-14T05:30:44.9121454Z   retention-days: 14
2025-03-14T05:30:44.9121640Z   if-no-files-found: ignore
2025-03-14T05:30:44.9121839Z   path: debug-*.zip
2025-03-14T05:30:44.9122012Z   name: artifact
2025-03-14T05:30:44.9122174Z   region: us-east-1
2025-03-14T05:30:44.9122342Z env:
2025-03-14T05:30:44.9122508Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:44.9122803Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:44.9123105Z ##[endgroup]
2025-03-14T05:30:45.1570690Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2025-03-14T05:30:45.1836092Z ##[group]Run # shellcheck disable=SC2156
2025-03-14T05:30:45.1836382Z # shellcheck disable=SC2156
2025-03-14T05:30:45.1836760Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-03-14T05:30:45.1841169Z shell: /usr/bin/bash -e {0}
2025-03-14T05:30:45.1841396Z env:
2025-03-14T05:30:45.1841569Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:45.1841879Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:45.1842194Z ##[endgroup]
2025-03-14T05:30:45.3258116Z Prepare all required actions
2025-03-14T05:30:45.3258473Z Getting action download info
2025-03-14T05:30:45.4600886Z ##[group]Run ./.github/actions/upload-utilization-stats
2025-03-14T05:30:45.4601154Z with:
2025-03-14T05:30:45.4601334Z   job_id: 38754841598
2025-03-14T05:30:45.4601666Z   job_name: linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:30:45.4602034Z   workflow_name: inductor
2025-03-14T05:30:45.4602228Z   workflow_run_id: 13849515380
2025-03-14T05:30:45.4602422Z   workflow_attempt: 1
2025-03-14T05:30:45.4602594Z env:
2025-03-14T05:30:45.4602755Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:45.4603047Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:45.4603358Z ##[endgroup]
2025-03-14T05:30:45.4624688Z ##[group]Run echo "workflow_id: 13849515380"
2025-03-14T05:30:45.4624964Z echo "workflow_id: 13849515380"
2025-03-14T05:30:45.4625227Z echo "workflow_attempt: 1"
2025-03-14T05:30:45.4625449Z echo "workflow_Name: inductor"
2025-03-14T05:30:45.4625669Z echo "job_id: 38754841598"
2025-03-14T05:30:45.4626065Z echo "job_name:  linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)"
2025-03-14T05:30:45.4630574Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:45.4630833Z env:
2025-03-14T05:30:45.4631013Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:45.4631317Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:45.4631631Z ##[endgroup]
2025-03-14T05:30:45.4655500Z workflow_id: 13849515380
2025-03-14T05:30:45.4659945Z workflow_attempt: 1
2025-03-14T05:30:45.4660557Z workflow_Name: inductor
2025-03-14T05:30:45.4660842Z job_id: 38754841598
2025-03-14T05:30:45.4661911Z job_name:  linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)
2025-03-14T05:30:45.4694002Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-14T05:30:45.4694236Z with:
2025-03-14T05:30:45.4694405Z   shell: bash
2025-03-14T05:30:45.4694580Z   timeout_minutes: 5
2025-03-14T05:30:45.4694765Z   max_attempts: 5
2025-03-14T05:30:45.4694961Z   retry_wait_seconds: 30
2025-03-14T05:30:45.4695264Z   command: set -eu
python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3

2025-03-14T05:30:45.4695580Z   polling_interval_seconds: 1
2025-03-14T05:30:45.4695780Z   warning_on_retry: true
2025-03-14T05:30:45.4695974Z   continue_on_error: false
2025-03-14T05:30:45.4696163Z env:
2025-03-14T05:30:45.4696324Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:45.4696617Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:45.4696925Z ##[endgroup]
2025-03-14T05:30:45.7303216Z Defaulting to user installation because normal site-packages is not writeable
2025-03-14T05:30:45.7462151Z Requirement already satisfied: python-dateutil==2.8.2 in /home/ec2-user/.local/lib/python3.9/site-packages (2.8.2)
2025-03-14T05:30:46.4114105Z Collecting boto3==1.35.42
2025-03-14T05:30:46.4139452Z   Using cached boto3-1.35.42-py3-none-any.whl (139 kB)
2025-03-14T05:30:46.4155135Z Requirement already satisfied: pandas==2.1.3 in /home/ec2-user/.local/lib/python3.9/site-packages (2.1.3)
2025-03-14T05:30:46.4163350Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0)
2025-03-14T05:30:46.4212841Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0)
2025-03-14T05:30:46.4214832Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4)
2025-03-14T05:30:46.4215567Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99)
2025-03-14T05:30:46.4933470Z Requirement already satisfied: tzdata>=2022.1 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (2025.1)
2025-03-14T05:30:46.4934365Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1)
2025-03-14T05:30:46.4941939Z Requirement already satisfied: numpy<2,>=1.22.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from pandas==2.1.3) (1.26.4)
2025-03-14T05:30:46.4995626Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10)
2025-03-14T05:30:46.5584203Z Installing collected packages: boto3
2025-03-14T05:30:46.5584583Z   Attempting uninstall: boto3
2025-03-14T05:30:46.5589627Z     Found existing installation: boto3 1.35.33
2025-03-14T05:30:46.5651036Z     Uninstalling boto3-1.35.33:
2025-03-14T05:30:46.5657562Z       Successfully uninstalled boto3-1.35.33
2025-03-14T05:30:46.6105721Z Successfully installed boto3-1.35.42
2025-03-14T05:30:47.5355653Z Command completed after 1 attempt(s).
2025-03-14T05:30:47.5420524Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T05:30:47.5420976Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-14T05:30:47.5421306Z   --workflow-run-id "13849515380" \
2025-03-14T05:30:47.5421546Z   --workflow-name "inductor" \
2025-03-14T05:30:47.5421779Z   --workflow-run-attempt "1" \
2025-03-14T05:30:47.5442275Z   --job-id "38754841598" \
2025-03-14T05:30:47.5442693Z   --job-name "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)"
2025-03-14T05:30:47.5447470Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:47.5447839Z env:
2025-03-14T05:30:47.5448016Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:47.5448325Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:47.5448642Z ##[endgroup]
2025-03-14T05:30:48.5665686Z repo: pytorch/pytorch
2025-03-14T05:30:48.5668596Z Downloading logs-test-cpu_inductor_torchbench-1-2-linux.8xlarge.amx_38754841598.zip
2025-03-14T05:30:48.5668989Z Converted Log Model: UtilizationMetadata:
2025-03-14T05:30:48.5669831Z  UtilizationMetadata(level='metadata', workflow_id='13849515380', job_id='38754841598', workflow_name='inductor', job_name='linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741925854, gpu_count=0, cpu_count=32, gpu_type='', error=None)
2025-03-14T05:30:48.5670708Z [Db Segments] detected pytest cmd: 14, generated segments: 14
2025-03-14T05:30:48.5670985Z [db model] Peek db timeseries 
2025-03-14T05:30:48.5671198Z :{
2025-03-14T05:30:48.5671365Z     "created_at": 1741930248,
2025-03-14T05:30:48.5671572Z     "type": "utilization",
2025-03-14T05:30:48.5671766Z     "tags": [
2025-03-14T05:30:48.5671933Z         "record"
2025-03-14T05:30:48.5672094Z     ],
2025-03-14T05:30:48.5672270Z     "time_stamp": 1741925854,
2025-03-14T05:30:48.5672474Z     "repo": "pytorch/pytorch",
2025-03-14T05:30:48.5672678Z     "workflow_id": 13849515380,
2025-03-14T05:30:48.5672877Z     "run_attempt": 1,
2025-03-14T05:30:48.5673061Z     "job_id": 38754841598,
2025-03-14T05:30:48.5673257Z     "workflow_name": "inductor",
2025-03-14T05:30:48.5673616Z     "job_name": "linux-jammy-cpu-py3.9-gcc11-inductor / test (cpu_inductor_torchbench, 1, 2, linux.8xlarge.amx)",
2025-03-14T05:30:48.5674312Z     "json_data": "{}"
2025-03-14T05:30:48.5674496Z }
2025-03-14T05:30:48.5674821Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515380/1/38754841598/metadata
2025-03-14T05:30:48.5675355Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13849515380/1/38754841598/metadata 
2025-03-14T05:30:48.5675903Z Writing 869 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515380/1/38754841598/time_series
2025-03-14T05:30:48.5676590Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13849515380/1/38754841598/time_series 
2025-03-14T05:30:48.6876218Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2025-03-14T05:30:48.6876535Z with:
2025-03-14T05:30:48.6876715Z env:
2025-03-14T05:30:48.6876898Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:48.6877234Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:48.6877583Z ##[endgroup]
2025-03-14T05:30:48.6898265Z ##[group]Run set -eou pipefail
2025-03-14T05:30:48.6898554Z set -eou pipefail
2025-03-14T05:30:48.6898773Z 
2025-03-14T05:30:48.6899055Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2025-03-14T05:30:48.6899407Z for _ in $(seq 1440); do
2025-03-14T05:30:48.6899658Z     # Break if no ssh session exists anymore
2025-03-14T05:30:48.6899913Z     if [ "$(who)" = "" ]; then
2025-03-14T05:30:48.6900138Z       break
2025-03-14T05:30:48.6900324Z     fi
2025-03-14T05:30:48.6900545Z     echo "."
2025-03-14T05:30:48.6900740Z     sleep 5
2025-03-14T05:30:48.6900927Z done
2025-03-14T05:30:48.6905785Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:48.6906048Z env:
2025-03-14T05:30:48.6906224Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:48.6906538Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:48.6906854Z ##[endgroup]
2025-03-14T05:30:48.6928870Z Holding runner for 2 hours until all ssh sessions have logged out
2025-03-14T05:30:48.6977175Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T05:30:48.6977636Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-14T05:30:48.6977923Z # shellcheck disable=SC2046
2025-03-14T05:30:48.6978161Z docker stop $(docker ps -q) || true
2025-03-14T05:30:48.6978405Z # Prune all of the docker images
2025-03-14T05:30:48.6978636Z docker system prune -af
2025-03-14T05:30:48.6983121Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:30:48.6983388Z env:
2025-03-14T05:30:48.6983570Z   GIT_DEFAULT_BRANCH: main
2025-03-14T05:30:48.6983891Z   DOCKER_CONTAINER_ID: 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:30:48.6984276Z ##[endgroup]
2025-03-14T05:30:59.4083472Z 2160b5b633d5
2025-03-14T05:31:00.5263234Z Deleted Containers:
2025-03-14T05:31:00.5263697Z 2160b5b633d5786e293fbd8f16e49757d0f31fc1403e3b489c8237accc15d231
2025-03-14T05:31:00.5263935Z 
2025-03-14T05:31:04.2043340Z Deleted Images:
2025-03-14T05:31:04.2047702Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks:aa89d6e739080d90fa18625d57297c6734465849
2025-03-14T05:31:04.2053194Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks@sha256:2f16eb7d476b5dc359eb789543b0cfc9aa5c04fe105d51acd219f91259bad5ab
2025-03-14T05:31:04.2054513Z deleted: sha256:1bab961b3bd90ec6714167a095e5437a0239700971887d1c663377ef708f8526
2025-03-14T05:31:04.2054970Z deleted: sha256:f1c70fafa21653c183da3a27b152e3132edfcc8f536568e599a8a7475a898486
2025-03-14T05:31:04.2055398Z deleted: sha256:257be7015a258302c070c7e2128106874db3320b42c0be587cde8a5c4668eb59
2025-03-14T05:31:04.2056095Z deleted: sha256:948eebb525eda26cfb174529301ea9c21fa30d7c38274102a4e84e8c5fbe8384
2025-03-14T05:31:04.2056520Z deleted: sha256:b9ac7767cf0b17526969b4b0803e8c07d3e794c502cfc420582474375cd9a813
2025-03-14T05:31:04.2056941Z deleted: sha256:80c0b71da98feea65ea51c56635d7acb37ad1eefb862adffe484b0a6ec73d382
2025-03-14T05:31:04.2057339Z deleted: sha256:9211eaf972d93a6b3e1157ed0733c6ef570902faf25a90ffcca263c3ecfe6a71
2025-03-14T05:31:04.2057721Z deleted: sha256:770a3a228550db1ce19da1d84c8ab8f7422a2d6b2177a744f04869331fdc011f
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2025-03-14T05:31:04.2092232Z Total reclaimed space: 41.73GB
2025-03-14T05:31:04.2165366Z Post job cleanup.
2025-03-14T05:31:04.2222925Z Post job cleanup.
2025-03-14T05:31:04.3450473Z [command]/usr/bin/git version
2025-03-14T05:31:04.3492236Z git version 2.47.1
2025-03-14T05:31:04.3525852Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/29262fa7-eea9-41bb-8d84-a822ba88c0bf/.gitconfig'
2025-03-14T05:31:04.3553533Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/29262fa7-eea9-41bb-8d84-a822ba88c0bf' before making global git config changes
2025-03-14T05:31:04.3557547Z Adding repository directory to the temporary git global config as a safe directory
2025-03-14T05:31:04.3560327Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-14T05:31:04.3610419Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-14T05:31:04.3653005Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'core\.sshCommand' && git config --local --unset-all 'core.sshCommand' || :"
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2025-03-14T05:31:04.7956555Z Entering 'third_party/pybind11'
2025-03-14T05:31:04.8007900Z Entering 'third_party/python-peachpy'
2025-03-14T05:31:04.8057227Z Entering 'third_party/sleef'
2025-03-14T05:31:04.8103516Z Entering 'third_party/tensorpipe'
2025-03-14T05:31:04.8151092Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T05:31:04.8208421Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T05:31:04.8250562Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T05:31:04.8302271Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T05:31:04.8347978Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T05:31:04.8420420Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-14T05:31:04.8445364Z http.https://github.com/.extraheader
2025-03-14T05:31:04.8452714Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-03-14T05:31:04.8486846Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'http\.https\:\/\/github\.com\/\.extraheader' && git config --local --unset-all 'http.https://github.com/.extraheader' || :"
2025-03-14T05:31:04.8791006Z Entering 'android/libs/fbjni'
2025-03-14T05:31:04.8833850Z http.https://github.com/.extraheader
2025-03-14T05:31:04.8867959Z Entering 'third_party/FP16'
2025-03-14T05:31:04.8923719Z http.https://github.com/.extraheader
2025-03-14T05:31:04.8959547Z Entering 'third_party/FXdiv'
2025-03-14T05:31:04.8988622Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9019759Z Entering 'third_party/NNPACK'
2025-03-14T05:31:04.9053642Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9087180Z Entering 'third_party/NVTX'
2025-03-14T05:31:04.9115115Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9151638Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-14T05:31:04.9180699Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9217829Z Entering 'third_party/XNNPACK'
2025-03-14T05:31:04.9250616Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9301816Z Entering 'third_party/benchmark'
2025-03-14T05:31:04.9333282Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9368419Z Entering 'third_party/composable_kernel'
2025-03-14T05:31:04.9400176Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9444813Z Entering 'third_party/cpp-httplib'
2025-03-14T05:31:04.9471934Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9502017Z Entering 'third_party/cpuinfo'
2025-03-14T05:31:04.9535228Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9571573Z Entering 'third_party/cudnn_frontend'
2025-03-14T05:31:04.9602474Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9639902Z Entering 'third_party/cutlass'
2025-03-14T05:31:04.9671455Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9717702Z Entering 'third_party/eigen'
2025-03-14T05:31:04.9746081Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9783895Z Entering 'third_party/fbgemm'
2025-03-14T05:31:04.9815619Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9847910Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-14T05:31:04.9875521Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9915131Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-14T05:31:04.9946214Z http.https://github.com/.extraheader
2025-03-14T05:31:04.9978657Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-14T05:31:05.0009330Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0047410Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-14T05:31:05.0074081Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0116689Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-14T05:31:05.0143761Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0178730Z Entering 'third_party/flash-attention'
2025-03-14T05:31:05.0213251Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0242421Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-14T05:31:05.0273132Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0315955Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-14T05:31:05.0356440Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0413588Z Entering 'third_party/flatbuffers'
2025-03-14T05:31:05.0441889Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0478872Z Entering 'third_party/fmt'
2025-03-14T05:31:05.0511487Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0543929Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-14T05:31:05.0577247Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0612176Z Entering 'third_party/gloo'
2025-03-14T05:31:05.0641399Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0676246Z Entering 'third_party/googletest'
2025-03-14T05:31:05.0707820Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0740069Z Entering 'third_party/ideep'
2025-03-14T05:31:05.0773750Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0805980Z Entering 'third_party/ideep/mkl-dnn'
2025-03-14T05:31:05.0838285Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0879022Z Entering 'third_party/ittapi'
2025-03-14T05:31:05.0912648Z http.https://github.com/.extraheader
2025-03-14T05:31:05.0945987Z Entering 'third_party/kineto'
2025-03-14T05:31:05.0974393Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1006849Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-14T05:31:05.1036606Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1073966Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-14T05:31:05.1103211Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1139020Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-14T05:31:05.1170541Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1205060Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-14T05:31:05.1236898Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1269440Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-14T05:31:05.1295403Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1341214Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-14T05:31:05.1379241Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1417451Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-14T05:31:05.1448472Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1481084Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-14T05:31:05.1515805Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1550891Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-14T05:31:05.1581837Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1618778Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
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2025-03-14T05:31:05.1688689Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-14T05:31:05.1718758Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1749783Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-14T05:31:05.1790475Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1826032Z Entering 'third_party/kleidiai'
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2025-03-14T05:31:05.1896546Z Entering 'third_party/mimalloc'
2025-03-14T05:31:05.1926170Z http.https://github.com/.extraheader
2025-03-14T05:31:05.1967132Z Entering 'third_party/nlohmann'
2025-03-14T05:31:05.2003365Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2033805Z Entering 'third_party/onnx'
2025-03-14T05:31:05.2065332Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2112982Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-14T05:31:05.2139333Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2183728Z Entering 'third_party/opentelemetry-cpp'
2025-03-14T05:31:05.2211663Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2246899Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-14T05:31:05.2273329Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2308911Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-14T05:31:05.2340222Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2372849Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-14T05:31:05.2405557Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2439520Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-14T05:31:05.2470172Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2511988Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-14T05:31:05.2540099Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2576048Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-14T05:31:05.2606093Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2640723Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-14T05:31:05.2678753Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2716963Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-14T05:31:05.2746076Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2784462Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-14T05:31:05.2811432Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2844980Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-14T05:31:05.2873524Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2927385Z Entering 'third_party/pocketfft'
2025-03-14T05:31:05.2959623Z http.https://github.com/.extraheader
2025-03-14T05:31:05.2994030Z Entering 'third_party/protobuf'
2025-03-14T05:31:05.3029515Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3063895Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-14T05:31:05.3095204Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3131803Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-14T05:31:05.3161389Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3218036Z Entering 'third_party/psimd'
2025-03-14T05:31:05.3229056Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3263593Z Entering 'third_party/pthreadpool'
2025-03-14T05:31:05.3298780Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3333476Z Entering 'third_party/pybind11'
2025-03-14T05:31:05.3362490Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3409306Z Entering 'third_party/python-peachpy'
2025-03-14T05:31:05.3441177Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3471830Z Entering 'third_party/sleef'
2025-03-14T05:31:05.3505094Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3540320Z Entering 'third_party/tensorpipe'
2025-03-14T05:31:05.3576087Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3609522Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-14T05:31:05.3639652Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3681257Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-14T05:31:05.3716303Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3750888Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-14T05:31:05.3782692Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3819070Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-14T05:31:05.3850425Z http.https://github.com/.extraheader
2025-03-14T05:31:05.3881827Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-14T05:31:05.3940627Z http.https://github.com/.extraheader
2025-03-14T05:31:05.4162117Z A job completed hook has been configured by the self-hosted runner administrator
2025-03-14T05:31:05.4186509Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2025-03-14T05:31:05.4190063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-14T05:31:05.4190345Z ##[endgroup]
2025-03-14T05:31:05.4265258Z [!ALERT!] Swap in detected! [!ALERT!]
2025-03-14T05:31:14.5198661Z [!ALERT!] Swap out detected [!ALERT!]
2025-03-14T05:31:28.7306503Z Cleaning up orphan processes